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  • 1
    Electronic Resource
    Electronic Resource
    A Formalism for Relevance and Its Application in Feature Subset Selection (2000)
    Springer
    Machine learning 41 (2000), S. 175-195 
    Details
    ISSN: 0885-6125
    Keywords: machine learning ; knowledge discovery ; data mining ; relevance ; feature subset selection
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The notion of relevance is used in many technical fields. In the areas of machine learning and data mining, for example, relevance is frequently used as a measure in feature subset selection (FSS). In previous studies, the interpretation of relevance has varied and its connection to FSS has been loose. In this paper a rigorous mathematical formalism is proposed for relevance, which is quantitative and normalized. To apply the formalism in FSS, a characterization is proposed for FSS: preservation of learning information and minimization of joint entropy. Based on the characterization, a tight connection between relevance and FSS is established: maximizing the relevance of features to the decision attribute, and the relevance of the decision attribute to the features. This connection is then used to design an algorithm for FSS. The algorithm is linear in the number of instances and quadratic in the number of features. The algorithm is evaluated using 23 public datasets, resulting in an improvement in prediction accuracy on 16 datasets, and a loss in accuracy on only 1 dataset. This provides evidence that both the formalism and its connection to FSS are sound.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1007612503587
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  • 2
    Electronic Resource
    Electronic Resource
    Maximizing Theory Accuracy Through Selective Reinterpretation (2000)
    Springer
    Machine learning 41 (2000), S. 123-152 
    Details
    ISSN: 0885-6125
    Keywords: logical theories ; theory revision ; probabilistic theories ; flawed domain theories ; approximate reasoning ; machine learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Existing methods for exploiting flawed domain theories depend on the use of a sufficiently large set of training examples for diagnosing and repairing flaws in the theory. In this paper, we offer a method of theory reinterpretation that makes only marginal use of training examples. The idea is as follows: Often a small number of flaws in a theory can completely destroy the theory's classification accuracy. Yet it is clear that valuable information is available even from such flawed theories. For example, an instance with severalindependent proofs in a slightly flawed theory is certainly more likely to be correctly classified as positive than an instance with only a single proof. This idea can be generalized to a numerical notion of “degree of provedness” which measures the robustness of proofs or refutations for a given instance. This “degree of provedness” can be easily computed using a “soft” interpretation of the theory. Given a ranking of instances based on the values so obtained, all that is required to classify instances is to determine some cutoff threshold above which instances are classified as positive. Such a threshold can be determined on the basis of a small set of training examples. For theories with a few localized flaws, we improve the method by “rehardening”: interpreting only parts of the theory softly, while interpreting the rest of the theory in the usual manner. Isolating those parts of the theory that should be interpreted softly can be done on the basis of a small number of training examples. Softening, with or without rehardening, can be used by itself as a quick way of handling theories with suspected flaws where few training examples are available. Additionally softening and rehardening can be used in conjunction with other methods as a meta-algorithm for determining which theory revision methods are appropriate for a given theory.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1007653300862
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  • 3
    Electronic Resource
    Electronic Resource
    Learning Algorithms for Keyphrase Extraction (2000)
    Springer
    Information retrieval 2 (2000), S. 303-336 
    Details
    ISSN: 1573-7659
    Keywords: machine learning ; summarization ; indexing ; keywords ; keyphrase extraction
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Many academic journals ask their authors to provide a list of about five to fifteen keywords, to appear on the first page of each article. Since these key words are often phrases of two or more words, we prefer to call them keyphrases. There is a wide variety of tasks for which keyphrases are useful, as we discuss in this paper. We approach the problem of automatically extracting keyphrases from text as a supervised learning task. We treat a document as a set of phrases, which the learning algorithm must learn to classify as positive or negative examples of keyphrases. Our first set of experiments applies the C4.5 decision tree induction algorithm to this learning task. We evaluate the performance of nine different configurations of C4.5. The second set of experiments applies the GenEx algorithm to the task. We developed the GenEx algorithm specifically for automatically extracting keyphrases from text. The experimental results support the claim that a custom-designed algorithm (GenEx), incorporating specialized procedural domain knowledge, can generate better keyphrases than a general-purpose algorithm (C4.5). Subjective human evaluation of the keyphrases generated by GenEx suggests that about 80% of the keyphrases are acceptable to human readers. This level of performance should be satisfactory for a wide variety of applications.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1009976227802
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  • 4
    Electronic Resource
    Electronic Resource
    An Adjustable Description Quality Measure for Pattern Discovery Using the AQ Methodology (2000)
    Springer
    Journal of intelligent information systems 14 (2000), S. 199-216 
    Details
    ISSN: 1573-7675
    Keywords: machine learning ; data mining ; learning from noisy data ; natural induction ; AQ learning ; decision rules ; separate and conquer
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In concept learning and data mining tasks, the learner is typically faced with a choice of many possible hypotheses or patterns characterizing the input data. If one can assume that training data contain no noise, then the primary conditions a hypothesis must satisfy are consistency and completeness with regard to the data. In real-world applications, however, data are often noisy, and the insistence on the full completeness and consistency of the hypothesis is no longer valid. In such situations, the problem is to determine a hypothesis that represents the best trade-off between completeness and consistency. This paper presents an approach to this problem in which a learner seeks rules optimizing a rule quality criterion that combines the rule coverage (a measure of completeness) and training accuracy (a measure of inconsistency). These factors are combined into a single rule quality measure through a lexicographical evaluation functional (LEF). The method has been implemented in the AQ18 learning system for natural induction and pattern discovery, and compared with several other methods. Experiments have shown that the proposed method can be easily tailored to different problems and can simulate different rule learners by modifying the parameter of the rule quality criterion.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008787919756
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  • 5
    Electronic Resource
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    Consistency and Completeness in Rough Sets (2000)
    Springer
    Journal of intelligent information systems 15 (2000), S. 207-220 
    Details
    ISSN: 1573-7675
    Keywords: knowledge discovery ; machine learning ; rough sets ; inconsistency
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Consistency and completeness are defined in the context of rough set theory and shown to be related to the lower approximation and upper approximation, respectively. A member of a composed set (union of elementary sets) that is consistent with respect to a concept, surely belongs to the concept. An element that is not a member of a composed set that is complete with respect to a concept, surely does not belong to the concept. A consistent rule and a complete rule are useful in addition to any other rules learnt to describe a concept. When an element satisfies the consistent rule, it surely belongs to the concept, and when it does not satisfy the complete rule, it surely does not belong to the concept. In other cases, the other learnt rules are used. The results in the finite universe are extended to the infinite universe, thus introducing a rough set model for the learning from examples paradigm. The results in this paper have application in knowledge discovery or learning from database environments that are inconsistent, but at the same time demand accurate and definite knowledge. This study of consistency and completeness in rough sets also lays the foundation for related work at the intersection of rough set theory and inductive logic programming.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008762812830
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  • 6
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    Predicting Chemical Parameters of River Water Quality from Bioindicator Data (2000)
    Springer
    Applied intelligence 13 (2000), S. 7-17 
    Details
    ISSN: 1573-7497
    Keywords: bioindicators ; machine learning ; regression trees ; rivers ; water quality
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We address the problem of inferring chemical parameters of river water quality from biological ones. This task is important for enabling selective chemical monitoring of river water quality. We apply machine learning, in particular regression tree induction, to biological and chemical data on the water quality of Slovenian rivers. Regression trees are constructed that predict values of chemical parameters from data on the presence of bioindicator taxa at the species and family levels.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008323212047
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  • 7
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    Application of Bayesian Network Learning Methods to Waste Water Treatment Plants (2000)
    Springer
    Applied intelligence 13 (2000), S. 19-40 
    Details
    ISSN: 1573-7497
    Keywords: Bayesian networks ; machine learning ; waste water treatment plants
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Bayesian Networks have been proposed as an alternative to rule-based systems in domains with uncertainty. Applications in monitoring and control can benefit from this form of knowledge representation. Following the work of Chong and Walley, we explore the possibilities of Bayesian Networks in the Waste Water Treatment Plants (WWTP) monitoring and control domain. We show the advantages of modelling knowledge in such a domain by means of Bayesian networks, put forth new methods for knowledge acquisition, describe their applications to a real waste water treatment plant and comment on the results. We also show how a Bayesian Network learning environment was used in the process and which characteristics of data in the domain suggested new ways of representing knowledge in network form but with uncertainty representations formalisms other than probability. The results of applying a possibilistic extension of current learning methods are also shown and compared.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008375228885
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  • 8
    Electronic Resource
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    Multiagent Systems: A Survey from a Machine Learning Perspective (2000)
    Springer
    Autonomous robots 8 (2000), S. 345-383 
    Details
    ISSN: 1573-7527
    Keywords: multiagent systems ; machine learning ; survey ; robotics ; intelligent agents ; robotic soccer ; pursuit domain ; homogeneous agents ; heterogeneous agents ; communicating agents
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract Distributed Artificial Intelligence (DAI) has existed as a subfield of AI for less than two decades. DAI is concerned with systems that consist of multiple independent entities that interact in a domain. Traditionally, DAI has been divided into two sub-disciplines: Distributed Problem Solving (DPS) focuses on the information management aspects of systems with several components working together towards a common goal; Multiagent Systems (MAS) deals with behavior management in collections of several independent entities, or agents. This survey of MAS is intended to serve as an introduction to the field and as an organizational framework. A series of general multiagent scenarios are presented. For each scenario, the issues that arise are described along with a sampling of the techniques that exist to deal with them. The presented techniques are not exhaustive, but they highlight how multiagent systems can be and have been used to build complex systems. When options exist, the techniques presented are biased towards machine learning approaches. Additional opportunities for applying machine learning to MAS are highlighted and robotic soccer is presented as an appropriate test bed for MAS. This survey does not focus exclusively on robotic systems. However, we believe that much of the prior research in non-robotic MAS is relevant to robotic MAS, and we explicitly discuss several robotic MAS, including all of those presented in this issue.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008942012299
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  • 9
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    Neural Network Agents for Learning Semantic Text Classification (2000)
    Springer
    Information retrieval 3 (2000), S. 87-103 
    Details
    ISSN: 1573-7659
    Keywords: neural networks ; news agent ; recurrent plausibility network ; text classification ; machine learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The research project AgNeT develops Agents for Neural Text routing in the internet. Unrestricted potentially faulty text messages arrive at a certain delivery point (e.g. email address or world wide web address). These text messages are scanned and then distributed to one of several expert agents according to a certain task criterium. Possible specific scenarios within this framework include the learning of the routing of publication titles or news titles. In this paper we describe extensive experiments for semantic text routing based on classified library titles and newswire titles. This task is challenging since incoming messages may contain constructions which have not been anticipated. Therefore, the contributions of this research are in learning and generalizing neural architectures for the robust interpretation of potentially noisy unrestricted messages. Neural networks were developed and examined for this topic since they support robustness and learning in noisy unrestricted real-world texts. We describe and compare different sets of experiments. The first set of experiments tests a recurrent neural network for the task of library title classification. Then we describe a larger more difficult newswire classification task from information retrieval. The comparison of the examined models demonstrates that techniques from information retrieval integrated into recurrent plausibility networks performed well even under noise and for different corpora.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1009942513170
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  • 10
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    A Trainable System for Object Detection (2000)
    Springer
    International journal of computer vision 38 (2000), S. 15-33 
    Details
    ISSN: 1573-1405
    Keywords: computer vision ; machine learning ; pattern recognition ; people detection ; face detection ; ear detection
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper presents a general, trainable system for object detection in unconstrained, cluttered scenes. The system derives much of its power from a representation that describes an object class in terms of an overcomplete dictionary of local, oriented, multiscale intensity differences between adjacent regions, efficiently computable as a Haar wavelet transform. This example-based learning approach implicitly derives a model of an object class by training a support vector machine classifier using a large set of positive and negative examples. We present results on face, people, and car detection tasks using the same architecture. In addition, we quantify how the representation affects detection performance by considering several alternate representations including pixels and principal components. We also describe a real-time application of our person detection system as part of a driver assistance system.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008162616689
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  • 11
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    MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions (2000)
    Springer
    Statistics and computing 10 (2000), S. 73-83 
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    ISSN: 1573-1375
    Keywords: clustering ; mixture modelling ; minimum message length ; MML ; Snob ; induction ; coding ; information theory ; statistical inference ; machine learning ; classification ; intrinsic classification ; unsupervised learning ; numerical taxonomy
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Minimum Message Length (MML) is an invariant Bayesian point estimation technique which is also statistically consistent and efficient. We provide a brief overview of MML inductive inference (Wallace C.S. and Boulton D.M. 1968. Computer Journal, 11: 185–194; Wallace C.S. and Freeman P.R. 1987. J. Royal Statistical Society (Series B), 49: 240–252; Wallace C.S. and Dowe D.L. (1999). Computer Journal), and how it has both an information-theoretic and a Bayesian interpretation. We then outline how MML is used for statistical parameter estimation, and how the MML mixture modelling program, Snob (Wallace C.S. and Boulton D.M. 1968. Computer Journal, 11: 185–194; Wallace C.S. 1986. In: Proceedings of the Nineteenth Australian Computer Science Conference (ACSC-9), Vol. 8, Monash University, Australia, pp. 357–366; Wallace C.S. and Dowe D.L. 1994b. In: Zhang C. et al. (Eds.), Proc. 7th Australian Joint Conf. on Artif. Intelligence. World Scientific, Singapore, pp. 37–44. See http://www.csse.monash.edu.au/-dld/Snob.html) uses the message lengths from various parameter estimates to enable it to combine parameter estimation with selection of the number of components and estimation of the relative abundances of the components. The message length is (to within a constant) the logarithm of the posterior probability (not a posterior density) of the theory. So, the MML theory can also be regarded as the theory with the highest posterior probability. Snob currently assumes that variables are uncorrelated within each component, and permits multi-variate data from Gaussian, discrete multi-category (or multi-state or multinomial), Poisson and von Mises circular distributions, as well as missing data. Additionally, Snob can do fully-parameterised mixture modelling, estimating the latent class assignments in addition to estimating the number of components, the relative abundances of the parameters and the component parameters. We also report on extensions of Snob for data which has sequential or spatial correlations between observations, or correlations between attributes.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008992619036
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  • 12
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    User Modeling for Adaptive News Access (2000)
    Springer
    User modeling and user adapted interaction 10 (2000), S. 147-180 
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    ISSN: 1573-1391
    Keywords: user modeling ; machine learning ; information retrieval ; intelligent agents ; recommender systems
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We present a framework for adaptive news access, based on machine learning techniques specifically designed for this task. First, we focus on the system's general functionality and system architecture. We then describe the interface and design of two deployed news agents that are part of the described architecture. While the first agent provides personalized news through a web-based interface, the second system is geared towards wireless information devices such as PDAs (personal digital assistants) and cell phones. Based on implicit and explicit user feedback, our agents use a machine learning algorithm to induce individual user models. Motivated by general shortcomings of other user modeling systems for Information Retrieval applications, as well as the specific requirements of news classification, we propose the induction of hybrid user models that consist of separate models for short-term and long-term interests. Furthermore, we illustrate how the described algorithm can be used to address an important issue that has thus far received little attention in the Information Retrieval community: a user's information need changes as a direct result of interaction with information. We empirically evaluate the system's performance based on data collected from regular system users. The goal of the evaluation is not only to understand the performance contributions of the algorithm's individual components, but also to assess the overall utility of the proposed user modeling techniques from a user perspective. Our results provide empirical evidence for the utility of the hybrid user model, and suggest that effective personalization can be achieved without requiring any extra effort from the user.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1026501525781
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  • 13
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    Visual Speech Synthesis by Morphing Visemes (2000)
    Springer
    International journal of computer vision 38 (2000), S. 45-57 
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    ISSN: 1573-1405
    Keywords: computer vision ; machine learning ; facial modelling ; facial animation ; morphing ; optical flow ; speech synthesis ; lip synchronization
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We present MikeTalk, a text-to-audiovisual speech synthesizer which converts input text into an audiovisual speech stream. MikeTalk is built using visemes, which are a small set of images spanning a large range of mouth shapes. The visemes are acquired from a recorded visual corpus of a human subject which is specifically designed to elicit one instantiation of each viseme. Using optical flow methods, correspondence from every viseme to every other viseme is computed automatically. By morphing along this correspondence, a smooth transition between viseme images may be generated. A complete visual utterance is constructed by concatenating viseme transitions. Finally, phoneme and timing information extracted from a text-to-speech synthesizer is exploited to determine which viseme transitions to use, and the rate at which the morphing process should occur. In this manner, we are able to synchronize the visual speech stream with the audio speech stream, and hence give the impression of a photorealistic talking face.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008166717597
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  • 14
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    Discovering Interesting Patterns for Investment Decision Making with GLOWER ☹—A Genetic Learner Overlaid with Entropy Reduction (2000)
    Springer
    Data mining and knowledge discovery 4 (2000), S. 251-280 
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    ISSN: 1573-756X
    Keywords: data mining ; knowledge discovery ; machine learning ; genetic algorithms ; financial prediction ; rule learning ; investment decision making ; systematic trading
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Prediction in financial domains is notoriously difficult for a number of reasons. First, theories tend to be weak or non-existent, which makes problem formulation open ended by forcing us to consider a large number of independent variables and thereby increasing the dimensionality of the search space. Second, the weak relationships among variables tend to be nonlinear, and may hold only in limited areas of the search space. Third, in financial practice, where analysts conduct extensive manual analysis of historically well performing indicators, a key is to find the hidden interactions among variables that perform well in combination. Unfortunately, these are exactly the patterns that the greedy search biases incorporated by many standard rule learning algorithms will miss. In this paper, we describe and evaluate several variations of a new genetic learning algorithm (GLOWER) on a variety of data sets. The design of GLOWER has been motivated by financial prediction problems, but incorporates successful ideas from tree induction and rule learning. We examine the performance of several GLOWER variants on two UCI data sets as well as on a standard financial prediction problem (S&P500 stock returns), using the results to identify one of the better variants for further comparisons. We introduce a new (to KDD) financial prediction problem (predicting positive and negative earnings surprises), and experiment with GLOWER, contrasting it with tree- and rule-induction approaches. Our results are encouraging, showing that GLOWER has the ability to uncover effective patterns for difficult problems that have weak structure and significant nonlinearities.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1009848126475
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  • 15
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    How do Simple Rules `Fit to Reality' in a Complex World? (1999)
    Springer
    Minds and machines 9 (1999), S. 543-564 
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    ISSN: 1572-8641
    Keywords: Bayesianism ; complexity ; decision theory ; fast and frugal heuristics ; machine learning ; philosophy of science ; predictive accuracy ; simplicity
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract The theory of fast and frugal heuristics, developed in a new book called Simple Heuristics that make Us Smart (Gigerenzer, Todd, and the ABC Research Group, in press), includes two requirements for rational decision making. One is that decision rules are bounded in their rationality –- that rules are frugal in what they take into account, and therefore fast in their operation. The second is that the rules are ecologically adapted to the environment, which means that they `fit to reality.' The main purpose of this article is to apply these ideas to learning rules–-methods for constructing, selecting, or evaluating competing hypotheses in science, and to the methodology of machine learning, of which connectionist learning is a special case. The bad news is that ecological validity is particularly difficult to implement and difficult to understand. The good news is that it builds an important bridge from normative psychology and machine learning to recent work in the philosophy of science, which considers predictive accuracy to be a primary goal of science.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008304819398
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  • 16
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    Named-Entity Recognition from Greek and English Texts (1999)
    Springer
    Journal of intelligent and robotic systems 26 (1999), S. 123-135 
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    ISSN: 1573-0409
    Keywords: named-entity recognition ; information extraction ; machine learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract Named-entity recognition (NER) involves the identification and classification of named entities in text. This is an important subtask in most language engineering applications, in particular information extraction, where different types of named entity are associated with specific roles in events. In this paper, we present a prototype NER system for Greek texts that we developed based on a NER system for English. Both systems are evaluated on corpora of the same domain and of similar size. The time-consuming process for the construction and update of domain-specific resources in both systems led us to examine a machine learning method for the automatic construction of such resources for a particular application in a specific language.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008124406923
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  • 17
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    Application of Machine Learning in Water Distribution Networks Assisted by Domain Experts (1999)
    Springer
    Journal of intelligent and robotic systems 26 (1999), S. 325-352 
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    ISSN: 1573-0409
    Keywords: machine learning ; water distribution network ; knowledge acquisition ; forecasting ; exception handling
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract The human-assisted application of machine learning techniques in the domain of water distribution networks is presented, corresponding to a research work done in the context of the European Esprit project WATERNET. One part of this project is a learning system that intends to capture knowledge from historic information collected during the operation of water distribution networks. The captured knowledge is expected to contribute to the improvement of the operation of the network. Presented ideas correspond to the first development phase of the learning system, focusing specially on the adopted methodology. The interactions between different classes of human experts and the learning system are also discussed. Finally some experimental results are presented.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008193214890
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  • 18
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    Multi-Layer Hierarchical Rule Learning in Reactive Robot Control Using Incremental Decision Trees (1999)
    Springer
    Journal of intelligent and robotic systems 24 (1999), S. 99-124 
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    ISSN: 1573-0409
    Keywords: behaviour decomposition ; behaviour learning ; intelligent navigation ; decision tress ; ITI ; machine learning ; robotics
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract This paper presents a new approach to the intelligent navigation of a mobile robot. The hybrid control architecture described combines properties of purely reactive and behaviour-based systems, providing the ability both to learn automatically behaviours from inception, and to capture these in a distributed hierarchy of decision tree networks. The robot is first trained in the simplest world which has no obstacles, and is then trained in successively more complex worlds, using the knowledge acquired in the previous worlds. Each world representing the perceptual space is thus directly mapped on a unique rule layer which represents in turn the robot action space encoded in a distinct decision tree. A major advantage of the current implementation, compared with the previous work, is that the generated rules are easily understood by human users. The paper demonstrates that the proposed behavioural decomposition approach provides efficient management of complex knowledge, and that the learning mechanism is able to cope with noise and uncertainty in sensory data.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008012519312
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  • 19
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    An Adaptable IE System to New Domains (1999)
    Springer
    Applied intelligence 10 (1999), S. 225-246 
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    ISSN: 1573-7497
    Keywords: information extraction ; automatic pattern acquisition ; machine learning ; EuroWordNet
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The most extended way of acquiring information for knowledge based systems is to do it manually. However, the high cost of this approach and the availability of alternative Knowledge Sources has lead to an increasing use of automatic acquisition approaches. In this paper we present M-TURBIO, a Text-Based Intelligent System (TBIS) that extracts information contained in restricted-domain documents. The system acquires part of its knowledge about the structure of the documents and the way the information is presented (i.e., syntactic-semantic rules) from a training set of these. Then, a database is created by means of applying these syntactic-semantic rules to extract the information contained in the whole document.
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    URL: http://dx.doi.org/10.1023/A:1008332021052
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  • 20
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    Comparing Structures Using a Hopfield-Style Neural Network (1999)
    Springer
    Applied intelligence 11 (1999), S. 15-30 
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    ISSN: 1573-7497
    Keywords: neural networks ; structured objects ; machine learning ; classification ; similarity ; nearest neighbor
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Labeled graphs are an appropriate and popular representation of structured objects in many domains. If the labels describe the properties of real world objects and their relations, finding the best match between two graphs turns out to be the weakly defined, NP-complete task of establishing a mapping between them that maps similar parts onto each other preserving as much as possible of their overall structural correspondence. In this paper, former approaches of structural matching and constraint relaxation by spreading activation in neural networks and the method of solving optimization tasks using Hopfield-style nets are combined. The approximate matching task is reformulated as the minimization of a quadratic energy function. The design of the approach enables the user to change the parameters and the dynamics of the net so that knowledge about matching preferences is included easily and transparently. In the last section, some examples demonstrate the successful application of this approach in classification and learning in the domain of organic chemistry.
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    URL: http://dx.doi.org/10.1023/A:1008320413168
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    Imputation of Missing Data in Industrial Databases (1999)
    Springer
    Applied intelligence 11 (1999), S. 259-275 
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    ISSN: 1573-7497
    Keywords: missing data ; industrial databases ; multiple imputation ; machine learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A limiting factor for the application of IDA methods in many domains is the incompleteness of data repositories. Many records have fields that are not filled in, especially, when data entry is manual. In addition, a significant fraction of the entries can be erroneous and there may be no alternative but to discard these records. But every cell in a database is not an independent datum. Statistical relationships will constrain and, often determine, missing values. Data imputation, the filling in of missing values for partially missing data, can thus be an invaluable first step in many IDA projects. New imputation methods that can handle the large-scale problems and large-scale sparsity of industrial databases are needed. To illustrate the incomplete database problem, we analyze one database with instrumentation maintenance and test records for an industrial process. Despite regulatory requirements for process data collection, this database is less than 50% complete. Next, we discuss possible solutions to the missing data problem. Several approaches to imputation are noted and classified into two categories: data-driven and model-based. We then describe two machine-learning-based approaches that we have worked with. These build upon well-known algorithms: AutoClass and C4.5. Several experiments are designed, all using the maintenance database as a common test-bed but with various data splits and algorithmic variations. Results are generally positive with up to 80% accuracies of imputation. We conclude the paper by outlining some considerations in selecting imputation methods, and by discussing applications of data imputation for intelligent data analysis.
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    URL: http://dx.doi.org/10.1023/A:1008334909089
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    Exploiting Hierarchy in Text Categorization (1999)
    Springer
    Information retrieval 1 (1999), S. 193-216 
    Details
    ISSN: 1573-7659
    Keywords: information retrieval ; text mining ; topic spotting ; text categorization ; knowledge management ; problem decomposition ; machine learning ; neural networks ; probabilistic models ; hierarchical models ; performance evaluation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract With the recent dramatic increase in electronic access to documents, text categorization—the task of assigning topics to a given document—has moved to the center of the information sciences and knowledge management. This article uses the structure that is present in the semantic space of topics in order to improve performance in text categorization: according to their meaning, topics can be grouped together into “meta-topics”, e.g., gold, silver, and copper are all metals. The proposed architecture matches the hierarchical structure of the topic space, as opposed to a flat model that ignores the structure. It accommodates both single and multiple topic assignments for each document. Its probabilistic interpretation allows its predictions to be combined in a principled way with information from other sources. The first level of the architecture predicts the probabilities of the meta-topic groups. This allows the individual models for each topic on the second level to focus on finer discriminations within the group. Evaluating the performance of a two-level implementation on the Reuters-22173 testbed of newswire articles shows the most significant improvement for rare classes.
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    URL: http://dx.doi.org/10.1023/A:1009983522080
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    The Connectionist Inductive Learning and Logic Programming System (1999)
    Springer
    Applied intelligence 11 (1999), S. 59-77 
    Details
    ISSN: 1573-7497
    Keywords: theory refinement ; machine learning ; artificial neural networks ; logic programming ; computational biology
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper presents the Connectionist Inductive Learning and Logic Programming System (C-IL2P). C-IL2P is a new massively parallel computational model based on a feedforward Artificial Neural Network that integrates inductive learning from examples and background knowledge, with deductive learning from Logic Programming. Starting with the background knowledge represented by a propositional logic program, a translation algorithm is applied generating a neural network that can be trained with examples. The results obtained with this refined network can be explained by extracting a revised logic program from it. Moreover, the neural network computes the stable model of the logic program inserted in it as background knowledge, or learned with the examples, thus functioning as a parallel system for Logic Programming. We have successfully applied C-IL2P to two real-world problems of computational biology, specifically DNA sequence analyses. Comparisons with the results obtained by some of the main neural, symbolic, and hybrid inductive learning systems, using the same domain knowledge, show the effectiveness of C-IL2P.
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    URL: http://dx.doi.org/10.1023/A:1008328630915
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    Knowledge Discovery in an Oceanographic Database (1999)
    Springer
    Applied intelligence 11 (1999), S. 135-148 
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    ISSN: 1573-7497
    Keywords: knowledge discovery ; machine learning ; texture ; feature selection ; image processing ; clusturing
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Knowledge discovery from image data is a multi-step iterative process. This paper describes the procedure we have used to develop a knowledge discovery system that classifies regions of the ocean floor based on textural features extracted from acoustic imagery. The image is subdivided into rectangular cells called texture elements (texels); a gray-level co-occurence matrix (GLCM) is computed for each texel in four directions. Secondary texture features are then computed from the GLCM resulting in a feature vector representation of each texel instance. Alternatively, a region-growing approach is used to identify irregularly shaped regions of varying size which have a homogenous texture and for which the texture features are computed. The Bayesian classifier Autoclass is used to cluster the instances. Feature extraction is one of the major tasks in knowledge discovery from images. The initial goal of this research was to identify regions of the image characterized by sand waves. Experiments were designed to use expert judgements to select the most effective set of features, to identify the best texel size, and to determine the number of meaningful classes in the data. The region-growing approach has proven to be more successful than the texel-based approach. This method provides a fast and accurate method for identifying provinces in the ocean floor of interest to geologists.
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    URL: http://dx.doi.org/10.1023/A:1008395129406
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    Data Mining in Large Databases Using Domain Generalization Graphs (1999)
    Springer
    Journal of intelligent information systems 13 (1999), S. 195-234 
    Details
    ISSN: 1573-7675
    Keywords: data mining ; knowledge discovery ; machine learning ; knowledge representation ; attribute-oriented generalization ; domain generalization graphs
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Attribute-oriented generalization summarizes the information in a relational database by repeatedly replacing specific attribute values with more general concepts according to user-defined concept hierarchies. We introduce domain generalization graphs for controlling the generalization of a set of attributes and show how they are constructed. We then present serial and parallel versions of the Multi-Attribute Generalization algorithm for traversing the generalization state space described by joining the domain generalization graphs for multiple attributes. Based upon a generate-and-test approach, the algorithm generates all possible summaries consistent with the domain generalization graphs. Our experimental results show that significant speedups are possible by partitioning path combinations from the DGGs across multiple processors. We also rank the interestingness of the resulting summaries using measures based upon variance and relative entropy. Our experimental results also show that these measures provide an effective basis for analyzing summary data generated from relational databases. Variance appears more useful because it tends to rank the less complex summaries (i.e., those with few attributes and/or tuples) as more interesting.
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    URL: http://dx.doi.org/10.1023/A:1008769516670
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    Neural Networks in Non-Euclidean Spaces (1999)
    Springer
    Neural processing letters 10 (1999), S. 201-210 
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    ISSN: 1573-773X
    Keywords: neural networks ; learning ; minimal distance methods ; similarity-based methods ; machine learning ; interpretation of neural functions ; classification
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Multilayer Perceptrons (MLPs) use scalar products to compute weighted activation of neurons providing decision borders using combinations of soft hyperplanes. The weighted fun-in activation function may be replaced by a distance function between the inputs and the weights, offering a natural generalization of the standard MLP model. Non-Euclidean distance functions may also be introduced by normalization of the input vectors into an extended feature space. Both approaches influence the shapes of decision borders dramatically. An illustrative example showing these changes is provided.
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    URL: http://dx.doi.org/10.1023/A:1018728407584
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    Learning Game-Specific Spatially-Oriented Heuristics (1998)
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    Constraints 3 (1998), S. 239-253 
    Details
    ISSN: 1572-9354
    Keywords: machine learning ; game playing ; spatial cognition ; extensible architectures
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper describes an architecture that begins with enough general knowledge to play any board game as a novice, and then shifts its decision-making emphasis to learned, game-specific, spatially-oriented heuristics. From its playing experience, it acquires game-specific knowledge about both patterns and spatial concepts. The latter are proceduralized as learned, spatially-oriented heuristics. These heuristics represent a new level of feature aggregation that effectively focuses the program's attention. While training against an external expert, the program integrates these heuristics robustly. After training it exhibits both a new emphasis on spatially-oriented play and the ability to respond to novel situations in a spatially-oriented manner. This significantly improves performance against a variety of opponents. In addition, we address the issue of context on pattern learning. The procedures described here move toward learning spatially-oriented heuristics for autonomous programs in other spatial domains.
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    URL: http://dx.doi.org/10.1023/A:1009734028965
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    The Frame Problem: An AI Fairy Tale (1998)
    Springer
    Minds and machines 8 (1998), S. 317-351 
    Details
    ISSN: 1572-8641
    Keywords: artificial intelligence ; frame problem ; causal induction ; machine learning ; logicism ; Bayesian learning ; MML
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract I analyze the frame problem and its relation to other epistemological problems for artificial intelligence, such as the problem of induction, the qualification problem and the "general" AI problem. I dispute the claim that extensions to logic (default logic and circumscriptive logic) will ever offer a viable way out of the problem. In the discussion it will become clear that the original frame problem is really a fairy tale: as originally presented, and as tools for its solution are circumscribed by Pat Hayes, the problem is entertaining, but incapable of resolution. The solution to the frame problem becomes available, and even apparent, when we remove artificial restrictions on its treatment and understand the interrelation between the frame problem and the many other problems for artificial epistemology. I present the solution to the frame problem: an adequate theory and method for the machine induction of causal structure. Whereas this solution is clearly satisfactory in principle, and in practice real progress has been made in recent years in its application, its ultimate implementation is in prospect only for future generations of AI researchers.
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    URL: http://dx.doi.org/10.1023/A:1008286921835
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    Learning from Innate Behaviors: A Quantitative Evaluation of Neural Network Controllers (1998)
    Springer
    Machine learning 31 (1998), S. 115-139 
    Details
    ISSN: 0885-6125
    Keywords: neural network controllers ; machine learning ; innateness ; biologically inspired robotics ; quantification in robotics
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The aim was to investigate a method of developing mobile robot controllers based on ideas about how plastic neural systems adapt to their environment by extracting regularities from the amalgamated behavior of inflexible (non-plastic) innate s ubsystems interacting with the world.Incremental bootstrapping of neural network controllers was examined. The objective was twofold. First, to develop and evaluate the use of prewired or innate robot controllers to bootstrap backpropagation learning for Multi-Layer Perceptron (MLP) controllers. Second, to develop and evaluate a new MLP controller trained on the back of another bootstrapped controller. The experimental hypothesis was that MLPs would improve on the performance of controllers used to train them. The performances of the innate and bootstrapped MLP controllers were compared in eight experiments on the tasks of avoiding obstacles and finding goals. Four quantitative measures were employed: the number of sensorimotor loops required to complete a task; the distance traveled; the mean distance from walls and obstacles; the smoothness of travel. The overall pattern of results from statistical analyses of these quantities su pported the hypothesis; the MLP controllers completed the tasks faster, smoother, and steered further from obstacles and walls than their innate teachers. In particular, a single MLP controller incrementally bootstrapped by a MLP subsumption controller was superior to the others.
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    URL: http://dx.doi.org/10.1023/A:1007444708590
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    Learning to Recognize Volcanoes on Venus (1998)
    Springer
    Machine learning 30 (1998), S. 165-194 
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    ISSN: 0885-6125
    Keywords: machine learning ; pattern recognition ; learning from examples ; large image databases ; data mining ; automatic cataloging ; detection of natural objects ; Magellan SAR ; JARtool ; volcanoes ; Venus ; principal components analysis ; trainable
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Dramatic improvements in sensor and image acquisition technology have created a demand for automated tools that can aid in the analysis of large image databases. We describe the development of JARtool, a trainable software system that learns to recognize volcanoes in a large data set of Venusian imagery. A machine learning approach is used because it is much easier for geologists to identify examples of volcanoes in the imagery than it is to specify domain knowledge as a set of pixel-level constraints. This approach can also provide portability to other domains without the need for explicit reprogramming; the user simply supplies the system with a new set of training examples. We show how the development of such a system requires a completely different set of skills than are required for applying machine learning to “toy world” domains. This paper discusses important aspects of the application process not commonly encountered in the “toy world,” including obtaining labeled training data, the difficulties of working with pixel data, and the automatic extraction of higher-level features.
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    URL: http://dx.doi.org/10.1023/A:1007400206189
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    A Fuzzy Diagnostic Model and Its Application in Automotive Engineering Diagnosis (1998)
    Springer
    Applied intelligence 9 (1998), S. 231-243 
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    ISSN: 1573-7497
    Keywords: fuzzy logic ; machine learning ; fault diagnosis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper describes a fuzzy diagnostic model that contains a fast fuzzy rule generation algorithm and a priority rule based inference engine. The fuzzy diagnostic model has been implemented in a fuzzy diagnostic system for the End-of-Line test at automobile assembly plants and the implemented system has been tested extensively and its performance is presented.
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    URL: http://dx.doi.org/10.1023/A:1008315803848
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    Incremental Feature Selection (1998)
    Springer
    Applied intelligence 9 (1998), S. 217-230 
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    ISSN: 1573-7497
    Keywords: pattern recognition ; machine learning ; feature selection ; dimensionality reduction
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Feature selection is a problem of finding relevant features. When the number of features of a dataset is large and its number of patterns is huge, an effective method of feature selection can help in dimensionality reduction. An incremental probabilistic algorithm is designed and implemented as an alternative to the exhaustive and heuristic approaches. Theoretical analysis is given to support the idea of the probabilistic algorithm in finding an optimal or near-optimal subset of features. Experimental results suggest that (1) the probabilistic algorithm is effective in obtaining optimal/suboptimal feature subsets; (2) its incremental version expedites feature selection further when the number of patterns is large and can scale up without sacrificing the quality of selected features.
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    URL: http://dx.doi.org/10.1023/A:1008363719778
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    Developing Mobile Robot Wall-Following Algorithms Using Genetic Programming (1998)
    Springer
    Applied intelligence 8 (1998), S. 33-41 
    Details
    ISSN: 1573-7497
    Keywords: genetic programming ; genetic algorithms ; computational genetics ; machine learning ; adaptive systems ; mobile robot ; robotics ; robot ; wall-following
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper demonstrates the use of genetic programming (GP) for the development of mobile robot wall-following behaviors. Algorithms are developed for a simulated mobile robot that uses an array of range finders for navigation. Navigation algorithms are tested in a variety of differently shaped environments to encourage the development of robust solutions, and reduce the possibility of solutions based on memorization of a fixed set of movements. A brief introduction to GP is presented. A typical wall-following robot evolutionary cycle is analyzed, and results are presented. GP is shown to be capable of producing robust wall-following navigation algorithms that perform well in each of the test environments used.
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    URL: http://dx.doi.org/10.1023/A:1008216530547
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    Learning from Innate Behaviors: A Quantitative Evaluation of Neural Network Controllers (1998)
    Springer
    Autonomous robots 5 (1998), S. 317-334 
    Details
    ISSN: 1573-7527
    Keywords: neural network controllers ; machine learning ; innateness ; biologically inspired robotics ; quantification in robotics
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract The aim was to investigate a method of developing mobile robot controllers based on ideas about how plastic neural systems adapt to their environment by extracting regularities from the amalgamated behavior of inflexible (nonplastic) innate subsystems interacting with the world. Incremental bootstrapping of neural network controllers was examined. The objective was twofold. First, to develop and evaluate the use of prewired or innate robot controllers to bootstrap backpropagation learning for Multilayer Perceptron (MLP) controllers. Second, to develop and evaluate a new MLP controller trained on the back of another bootstrapped controller. The experimental hypothesis was that MLPs would improve on the performance of controllers used to train them. The performances of the innate and bootstrapped MLP controllers were compared in eight experiments on the tasks of avoiding obstacles and finding goals. Four quantitative measures were employed: the number of sensorimotor loops required to complete a task; the distance traveled; the mean distance from walls and obstacles; the smoothness of travel. The overall pattern of results from statistical analyses of these quantities supported the hypothesis; the MLP controllers completed the tasks faster, smoother, and steered further from obstacles and walls than their innate teachers. In particular, a single MLP controller incrementally bootstrapped by a MLP subsumption controller was superior to the others.
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    First Order Regression (1997)
    Springer
    Machine learning 26 (1997), S. 147-176 
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    ISSN: 0885-6125
    Keywords: machine learning ; inductive logic programming ; regression ; real-valued variables ; first-order logic ; applications of machine learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We present a new approach, called First Order Regression (FOR), to handling numerical information in Inductive Logic Programming (ILP). FOR is a combination of ILP and numerical regression. First-order logic descriptions are induced to carve out those subspaces that are amenable to numerical regression among real-valued variables. The program FORS is an implementation of this idea, where numerical regression is focused on a distinguished continuous argument of the target predicate. We show that this can be viewed as a generalisation of the usual ILP problem. Applications of FORS on several real-world data sets are described: the prediction of mutagenicity of chemicals, the modelling of liquid dynamics in a surge tank, predicting the roughness in steel grinding, finite element mesh design, and operator's skill reconstruction in electric discharge machining. A comparison of FORS' performance with previous results in these domains indicates that FORS is an effective tool for ILP applications that involve numerical data.
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    PLATA: An Application of LEGAL, a Machine Learning Based System, to a Typology of Archaeological Ceramics (1997)
    Springer
    Computers and the humanities 31 (1997), S. 169-187 
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    ISSN: 1572-8412
    Keywords: archaeological typology ; ceramics ; knowledge acquisition ; machine learning ; Sudan
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Media Resources and Communication Sciences, Journalism
    Notes: Abstract The authors here show that machine learning techniques can be used for designing an archaeological typology, at an early stage when the classes are not yet well defined. The program (LEGAL, LEarning with GAlois Lattice) is a machine learning system which uses a set of examples and counter-examples in order to discriminate between classes. Results show a good compatibility between the classes such as the yare defined by the system and the archaeological hypotheses.
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    Cognitive Architecture for Robust Adaptive Control of Robots in a Team (1997)
    Springer
    Journal of intelligent and robotic systems 20 (1997), S. 251-273 
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    ISSN: 1573-0409
    Keywords: robot control ; adaptive behavior ; robust intelligent control ; multi-robot systems ; machine learning ; neural networks ; genetic algorithms ; cognitive architecture.
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract The objective of this paper is to present a cognitive architecture thatutilizes three different methodologies for adaptive, robust control ofrobots behaving intelligently in a team. The robots interact within a worldof objects, and obstacles, performing tasks robustly, while improving theirperformance through learning. The adaptive control of the robots has beenachieved by a novel control system. The Tropism-based cognitive architecturefor the individual behavior of robots in a colony is demonstrated throughexperimental investigation of the robot colony. This architecture is basedon representation of the likes and dislikes of the robots. It is shown thatthe novel architecture is not only robust, but also provides the robots withintelligent adaptive behavior. This objective is achieved by utilization ofthree different techniques of neural networks, machine learning, and geneticalgorithms. Each of these methodologies are applied to the tropismarchitecture, resulting in improvements in the task performance of the robotteam, demonstrating the adaptability and robustness of the proposed controlsystem.
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    Learning to Understand Information on the Internet: An Example-Based Approach (1997)
    Springer
    Journal of intelligent information systems 8 (1997), S. 133-153 
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    ISSN: 1573-7675
    Keywords: machine learning ; internet
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The explosive growth of the Web has made intelligent softwareassistants increasingly necessary for ordinary computer users. Bothtraditional approaches—search engines, hierarchical indices—andintelligent software agents require significant amounts of humaneffort to keep up with the Web. As an alternative, we investigate theproblem of automatically learning to interact with informationsources on the Internet. We report on ShopBotand ILA , two implemented agents that learn touse such resources. ShopBot learns how to extract information from onlinevendors using only minimal knowledge about product domains. Giventhe home pages of several online stores, ShopBotautonomously learns how to shop at those vendors. After its learningis complete, ShopBot is able to speedily visitover a dozen software stores and CD vendors, extract productinformation, and summarize the results for the user. ILAlearns to translate information from Internetsources into its own internal concepts. ILAbuilds a model of an information source that specifies the translation between the source's output and ILA 's model of the world. ILA iscapable of leveraging a small amount of knowledge about a domain tolearn models of many information sources. We show that ILA 's learning is fast and accurate, requiring only a smallnumber of queries per information source.
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    Comparative Performance of Rule Quality Measures in an Induction System (1997)
    Springer
    Applied intelligence 7 (1997), S. 113-124 
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    ISSN: 1573-7497
    Keywords: intelligent manufacturing ; rule quality ; machine learning ; induction ; post-processing
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper addresses an important problem related to the use ofinduction systems in analyzing real world data. The problem is thequality and reliability of the rules generated by the systems.~Wediscuss the significance of having a reliable and efficient rule quality measure. Such a measure can provide useful support ininterpreting, ranking and applying the rules generated by aninduction system. A number of rule quality and statistical measuresare selected from the literature and their performance is evaluatedon four sets of semiconductor data. The primary goal of thistesting and evaluation has been to investigate the performance ofthese quality measures based on: (i) accuracy, (ii) coverage, (iii)positive error ratio, and (iv) negative error ratio of the ruleselected by each measure. Moreover, the sensitivity of these qualitymeasures to different data distributions is examined. Inconclusion, we recommend Cohen‘s statistic as being the best qualitymeasure examined for the domain. Finally, we explain some future workto be done in this area.
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    On the Accuracy of Meta-learning for Scalable Data Mining (1997)
    Springer
    Journal of intelligent information systems 8 (1997), S. 5-28 
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    ISSN: 1573-7675
    Keywords: machine learning ; meta-learning ; scalability ; data mining ; classifiers
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In this paper, wedescribe a general approach to scaling data mining applications thatwe have come to call meta-learning. Meta-Learningrefers to a general strategy that seeks to learn how to combine anumber of separate learning processes in an intelligent fashion. Wedesire a meta-learning architecture that exhibits two key behaviors.First, the meta-learning strategy must produce an accurate final classification system. This means that a meta-learning architecturemust produce a final outcome that is at least as accurate as aconventional learning algorithm applied to all available data.Second, it must be fast, relative to an individual sequential learningalgorithm when applied to massive databases of examples, and operatein a reasonable amount of time. This paper focussed primarily onissues related to the accuracy and efficacy of meta-learning as ageneral strategy. A number of empirical results are presenteddemonstrating that meta-learning is technically feasible in wide-area,network computing environments.
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    Control-Sensitive Feature Selection for Lazy Learners (1997)
    Springer
    Artificial intelligence review 11 (1997), S. 227-253 
    Details
    ISSN: 1573-7462
    Keywords: lazy learning ; feature selection ; nearest neighbor ; induction ; machine learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract High sensitivity to irrelevant features is arguably the main shortcoming of simple lazy learners. In response to it, many feature selection methods have been proposed, including forward sequential selection (FSS) and backward sequential selection (BSS). Although they often produce substantial improvements in accuracy, these methods select the same set of relevant features everywhere in the instance space, and thus represent only a partial solution to the problem. In general, some features will be relevant only in some parts of the space; deleting them may hurt accuracy in those parts, but selecting them will have the same effect in parts where they are irrelevant. This article introduces RC, a new feature selection algorithm that uses a clustering-like approach to select sets of locally relevant features (i.e., the features it selects may vary from one instance to another). Experiments in a large number of domains from the UCI repository show that RC almost always improves accuracy with respect to FSS and BSS, often with high significance. A study using artificial domains confirms the hypothesis that this difference in performance is due to RC's context sensitivity, and also suggests conditions where this sensitivity will and will not be an advantage. Another feature of RC is that it is faster than FSS and BSS, often by an order of magnitude or more.
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    URL: http://dx.doi.org/10.1023/A:1006508722917
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    Automatically configuring constraint satisfaction programs: A case study (1996)
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    Constraints 1 (1996), S. 7-43 
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    ISSN: 1572-9354
    Keywords: constraint satisfaction algorithms ; machine learning ; configurable systems
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Multi-tac is a learning system that synthesizes heuristic constraint satisfaction programs. The system takes a library of generic algorithms and heuristics and specializes them for a particular application. We present a detailed case study with three different distributions of a single combinatorial problem, “Minimum Maximal Matching”, and show that Muti-tac can synthesize programs for these different distributions that perform on par with hand-coded programs and that exceed the performance of some well-known satisfiability algorithms. In synthesizing a program, Multi-tac bases its choice of heuristics on an instance distribution, and we demonstrate that this capability has a significant impact on the results.
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    URL: http://dx.doi.org/10.1007/BF00143877
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    Real-World Robotics: Learning to Plan for Robust Execution (1996)
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    Machine learning 23 (1996), S. 121-161 
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    ISSN: 0885-6125
    Keywords: machine learning ; robotics ; uncertainty ; planning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In executing classical plans in the real world, small discrepancies between a planner's internal representations and the real world are unavoidable. These can conspire to cause real-world failures even though the planner is sound and, therefore, proves that a sequence of actions achieves the goal. Permissive planning, a machine learning extension to classical planning, is one response to this difficulty. This paper describes the permissive planning approach and presents GRASPER, a permissive planning robotic system that learns to robustly pick up novel objects.
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    Machine learning applications in anthropology: Automated discovery over kinship structures (1996)
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    Computers and the humanities 30 (1996), S. 401-406 
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    ISSN: 1572-8412
    Keywords: machine learning ; induction ; inductive logic programming ; FOIL
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Media Resources and Communication Sciences, Journalism
    Notes: Abstract A common problem in anthropological field work is generalizing rules governing social interactions and relations (particularly kinship) from a series of examples. One class of machine learning algorithms is particularly well-suited to this task: inductive logic programming systems, as exemplified by FOIL. A knowledge base of relationships among individuals is established, in the form of a series of single-predicate facts. Given a set of positive and negative examples of a new relationship, the machine learning programs build a Horn clause description of the target relationship. The power of these algorithms to derive complex hypotheses is demonstrated for a set of kinship relationships drawn from the anthropological literature. FOIL extends the capabilities of earlier anthropology-specific learning programs by providing a more powerful representation for induced relationships, and is better able to learn in the face of noisy or incomplete data.
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    Real-world robotics: Learning to plan for robust execution (1996)
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    Machine learning 23 (1996), S. 121-161 
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    ISSN: 0885-6125
    Keywords: machine learning ; robotics ; uncertainty ; planning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In executing classical plans in the real world, small discrepancies between a planner's internal representations and the real world are unavoidable. These can conspire to cause real-world failures even though the planner is sound and, therefore, “proves” that a sequence of actions achieves the goal. Permissive planning, a machine learning extension to classical planning, is one response to this difficulty. This paper describes the permissive planning approach and presents GRASPER, a permissive planning robotic system that learns to robustly pick up novel objects.
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    URL: http://dx.doi.org/10.1007/BF00117442
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    On the worst-case analysis of temporal-difference learning algorithms (1996)
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    Machine learning 22 (1996), S. 95-121 
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    ISSN: 0885-6125
    Keywords: machine learning ; temporal-difference learning ; on-line learning ; worst-case analysis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We study the behavior of a family of learning algorithms based on Sutton's method of temporal differences. In our on-line learning framework, learning takes place in a sequence of trials, and the goal of the learning algorithm is to estimate a discounted sum of all the reinforcements that will be received in the future. In this setting, we are able to prove general upper bounds on the performance of a slightly modified version of Sutton's so-called TD(λ) algorithm. These bounds are stated in terms of the performance of the best linear predictor on the given training sequence, and are proved without making any statistical assumptions of any kind about the process producing the learner's observed training sequence. We also prove lower bounds on the performance of any algorithm for this learning problem, and give a similar analysis of the closely related problem of learning to predict in a model in which the learner must produce predictions for a whole batch of observations before receiving reinforcement.
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    On the Worst-Case Analysis of Temporal-Difference Learning Algorithms (1996)
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    Machine learning 22 (1996), S. 95-121 
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    ISSN: 0885-6125
    Keywords: machine learning ; temporal-difference learning ; on-line learning ; worst-case analysis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We study the behavior of a family of learning algorithms based on Sutton‘s method of temporal differences. In our on-line learning framework, learning takes place in a sequence of trials, and the goal of the learning algorithm is to estimate a discounted sum of all the reinforcements that will be received in the future. In this setting, we are able to prove general upper bounds on the performance of a slightly modified version of Sutton‘s so-called TD((gl) algorithm. These bounds are stated in terms of the performance of the best linear predictor on the given training sequence, and are proved without making any statistical assumptions of any kind about the process producing the learner‘s observed training sequence. We also prove lower bounds on the performance of any algorithm for this learning problem, and give a similar analysis of the closely related problem of learning to predict in a model in which the learner must produce predictions for a whole batch of observations before receiving reinforcement.
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    URL: http://dx.doi.org/10.1023/A:1018060205686
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    Average-case improvements when integrating ML and KA (1996)
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    Applied intelligence 6 (1996), S. 87-99 
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    ISSN: 1573-7497
    Keywords: machine learning ; knowledge acquisition ; integration ; models ; knowledge-based expert systems
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In this paper we develop a mathematical analysis, based on empirical measurements, of expected average improvements when integrating Machine Learning and Knowledge Acquisition systems in real-life domains. The analysis is based on the characteristics of component systems and combining techniques. Important characteristics include the accuracy of component systems, the degree to which component systems complement each other's weaknesses, and the ability of the combining mechanism to make good choices among competing component systems. Empirical measurements in a real-life application, in the Sendzimir rolling mill, have shown that integrating both approaches enables significant improvements. Improvements when combining systems in two oncological domains were smaller, yet positive again. Analytical average-case integrated models consisting of two systems are introduced. Conditions for improvements over the best, average and the worst system are established and the expected gains are analytically computed based on expected performances. Models strongly suggest that a reasonable integration of two systems offers significant improvements over the best single system in many or even most real-life domains.
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    URL: http://dx.doi.org/10.1007/BF00117810
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    Efficient estimation and model selection in large graphical models (1996)
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    Statistics and computing 6 (1996), S. 313-323 
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    ISSN: 1573-1375
    Keywords: Graphical models ; probabilistic expert systems ; machine learning ; Markov models ; causal structure
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We develop a computationally efficient method to determine the interaction structure in a multidimensional binary sample. We use an interaction model based on orthogonal functions, and give a result on independence properties in this model. Using this result we develop an efficient approximation algorithm for estimating the parameters in a given undirected model. To find the best model, we use a heuristic search algorithm in which the structure is determined incrementally. We also give an algorithm for reconstructing the causal directions, if such exist. We demonstrate that together these algorithms are capable of discovering almost all of the true structure for a problem with 121 variables, including many of the directions.
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    INSTRUCT: Modeling students by asking questions (1996)
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    User modeling and user adapted interaction 6 (1996), S. 273-302 
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    ISSN: 1573-1391
    Keywords: student modeling ; intelligent tutoring systems ; machine learning ; procedure induction from traces ; model tracing ; reconstructive modeling
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The paper reports an approach to inducing models of procedural skills from observed student performance. The approach, referred to as INSTRUCT, builds on two well-known techniques, reconstructive modeling and model tracing, at the same time avoiding their major pitfalls. INSTRUCT does not require prior empirical knowledge of student errors and is also neutral with respect to pedagogy and reasoning strategies applied by the student. Pedagogical actions and the student model are generated on-line, which allows for dynamic adaptation of instruction, problem generation and immediate feedback on student's errors. Furthermore, the approach is not only incremental but truly active, since it involves students in explicit dialogues about problem-solving decisions. Student behaviour is used as a source of information for user modeling and to compensate for the unreliability of the student model. INSTRUCT uses both implicit information about the steps the student performed or the explanations he or she asked for, and explicit information gained from the student's answers to direct question about operations being performed. Domain knowledge and the user model are used to focus the search on the portion of the problem space the student is likely to traverse while solving the problem at hand. The approach presented is examined in the context of SINT, an ITS for the domain of symbolic integration.
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    Extracting knowledge from examples: Induction of heuristic rules for wheelchair prescription (1995)
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    Journal of intelligent and robotic systems 14 (1995), S. 133-153 
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    ISSN: 1573-0409
    Keywords: Wheelchair prescription ; ID3 ; machine learning ; expert system ; rehabilitation ; equipment selection ; induction
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract The feasibility of using an induction algorithm to discover heuristic rules for wheelchair equipment selection is investigated. Syntactical rules for two description languages (one to describe the disabled client and another to describe wheelchair equipment configurations) are presented. These languages allow the rulebase developer to describe training instances (examples) to the computer in an intelligible way. An induction learning algorithm is used to classify these training instances, thereby producing a decision tree. Heuristic rules can then be extracted from the tree and placed in a rulebase for an expert system called LEADER. LEADER is being developed to aid a wheelchair prescriber in the equipment selection process. There are two important objectives of this research: (1) to reduce the time and facilitate the development of an intelligent expert system rulebase by extracting knowledge embedded within existing examples and (2) to provide the expert system with the ability to learn new rules autonomously. The ability to learn makes the rulebase dynamic; the initial rulebase would be augmented with new rules as additional examples are provided to the system while it is in clinical use.
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    Classification Accuracy: Machine Learning vs. Explicit Knowledge Acquisition (1995)
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    Machine learning 18 (1995), S. 109-114 
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    ISSN: 0885-6125
    Keywords: Expert systems ; machine learning ; explicit vs ; implicit knowledge acquisition ; classification accuracy
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This empirical study provides evidence that machine learning models can provide better classification accuracy than explicit knowledge acquisition techniques. The findings suggest that the main contribution of machine learning to expert systems is not just cost reduction, but rather the provision of tools for the development of better expert systems.
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    URL: http://dx.doi.org/10.1023/A:1022826724635
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    Classification accuracy: Machine learning vs. explicit knowledge acquisition (1995)
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    Machine learning 18 (1995), S. 109-114 
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    ISSN: 0885-6125
    Keywords: Expert systems ; machine learning ; explicit vs. implicit knowledge acquisition ; classification accuracy
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This empirical study provides evidence that machine learning models can provide better classification accuracy than explicit knowledge acquisition techniques. The findings suggest that the main contribution of machine learning to expert systems is not just cost reduction, but rather the provision of tools for the development of better expert systems.
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    Learning from a Population of Hypotheses (1995)
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    Machine learning 18 (1995), S. 255-276 
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    ISSN: 0885-6125
    Keywords: machine learning ; computational learning theory ; PAC learning ; learning agents
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We introduce a new formal model in which a learning algorithm must combine a collection of potentially poor but statistically independent hypothesis functions in order to approximate an unknown target function arbitrarily well. Our motivation includes the question of how to make optimal use of multiple independent runs of a mediocre learning algorithm, as well as settings in which the many hypotheses are obtained by a distributed population of identical learning agents.
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    Learning from a population of hypotheses (1995)
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    Machine learning 18 (1995), S. 255-276 
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    ISSN: 0885-6125
    Keywords: machine learning ; computational learning theory ; PAC learning ; learning agents
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We introduce a new formal model in which a learning algorithm must combine a collection of potentially poor but statistically independent hypothesis functions in order to approximate an unknown target function arbitrarily well. Our motivation includes the question of how to make optimal use of multiple independent runs of a mediocre learning algorithm, as well as settings in which the many hypotheses are obtained by a distributed population of identical learning agents.
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    Segmentation and tracking of piglets in images (1995)
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    Machine vision and applications 8 (1995), S. 187-193 
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    ISSN: 1432-1769
    Keywords: Tracking ; Segmentation ; Pigs ; Animals ; Computer vision
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract An algorithm was developed for the segmentation and tracking of piglets and tested on a 200-image sequence of 10 piglets moving on a straw background. The image-capture rate was 1 image/140 ms. The segmentation method was a combination of image differencing with respect to a median background and a Laplacian operator. The features tracked were blob edges in the segmented image. During tracking, the piglets were modelled as ellipses initialised on the blobs. Each piglet was tracked by searching for blob edges in an elliptical window about the piglet's position, which was predicted from its previous two positions.
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    Learning, goals, and learning goals: A perspective on goal-driven learning (1995)
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    Artificial intelligence review 9 (1995), S. 387-422 
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    ISSN: 1573-7462
    Keywords: machine learning ; cognitive modeling ; metacognition ; active learning ; multistrategy learning ; utility of learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In cognitive science, artificial intelligence, psychology, and education, a growing body of research supports the view that the learning process is strongly influenced by the learner's goals. The fundamental tenet ofgoal-driven learning is that learning is largely an active and strategic process in which the learner, human or machine, attempts to identify and satisfy its information needs in the context of its tasks and goals, its prior knowledge, its capabilities, and environmental opportunities for learning. This article examines the motivations for adopting a goal-driven model of learning, the relationship between task goals and learning goals, the influences goals can have on learning, and the pragmatic implications of the goal-driven learning model. It presents a new integrative framework for understanding the goal-driven learning process and applies this framework to characterizing research on goal-driven learning.
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    Discovering dynamics: From inductive logic programming to machine discovery (1995)
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    Journal of intelligent information systems 4 (1995), S. 89-108 
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    ISSN: 1573-7675
    Keywords: machine discovery ; machine learning ; dynamical system identification
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Machine discovery systems help humans to find natural laws from collections of experimentally collected data. Most of the laws found by existing machine discovery systems describe static situations, where a physical system has reached equilibrium. In this paper, we consider the problem of discovering laws that govern the behavior of dynamical systems, i.e., systems that change their state over time. Based on ideas from inductive logic programming and machine discovery, we present two systems, QMN and LAGRANGE, for discovery of qualitative and quantitative laws from quantitative (numerical) descriptions of dynamical system behavior. We illustrate their use by generating a variety of dynamical system models from example behaviors.
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    Induction of ripple-down rules applied to modeling large databases (1995)
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    Journal of intelligent information systems 5 (1995), S. 211-228 
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    ISSN: 1573-7675
    Keywords: inductive database modeling ; induction ; machine learning ; medical diagnosis ; ripple-down rules ; rules with exceptions ; Induct ; Garvan thyroid database
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A methodology forthe modeling of large data sets is described which results in rule sets having minimal inter-rule interactions, and being simply maintained. An algorithm for developing such rule sets automatically is described and its efficacy shown with standard test data sets. Comparative studies of manual and automatic modeling of a data set of some nine thousand five hundred cases are reported. A study is reported in which ten years of patient data have been modeled on a month by month basis to determine how well a diagnostic system developed by automated induction would have performed had it been in use throughout the project.
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    A Bayesian method for learning belief networks that contain hidden variables (1995)
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    Journal of intelligent information systems 4 (1995), S. 71-88 
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    ISSN: 1573-7675
    Keywords: probabilistic networks ; Bayesian belief networks ; hidden variables ; machine learning ; induction
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper presents a Bayesian method for computing the probability of a Bayesian belief-network structure from a database. In particular, the paper focuses on computing the probability of a belief-network structure that contains a hidden (latent) variable. A hidden variable represents a postulated entity that has not been directly measured. After reviewing related techniques, which previously were reported, this paper presents a new, more efficient method for handling hidden variables in belief networks.
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    Hierarchical integration of sensor data and contextual information for automatic target recognition (1995)
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    Applied intelligence 5 (1995), S. 269-290 
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    ISSN: 1573-7497
    Keywords: automatic target recognition ; machine learning ; abductive polynomial networks ; expert systems ; information fusion
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Real-time assessment of high-value targets is an ongoing challenge for the defense community. Many automatic target recognition (ATR) approaches exist, each with specific advantages and limitations. An ATR system is presented here that integrates machine learning, expert systems, and other advanced image understanding concepts. The ATR system employs a hierarchical strategy relying primarily on abductive polynomial networks at each level of recognition. Advanced feature extraction algorithms are used at each level for pixel characterization and target description. Polynomial networks process feature data and situational information, providing input for subsequent levels of processing. An expert system coordinates individual recognition modules. Heuristic processing of object likelihood estimates is also discussed. Here, separate estimators determine the likelihood that an object belongs to a particular class. Heuristic knowledge to resolve ambiguities that occur when more than one class appears likely is discussed. In addition, a comparison of model-based recognition with the primary polynomial network approach is presented. Model-based recognition is a goal-driven approach that compares a representation of the unknown target to a reference library of known targets. Each approach has advantages and limitations that should be considered for a specific implementation. This ATR approach can potentially overcome limitations of current systems such as catastrophic degradation during unanticipated operating conditions, while meeting strict processing requirements. These benefits result from implementation of robust feature extraction algorithms that do not take explicit advantage of peculiar characteristics of the sensor imagery; and the compact, real-time processing capability provided by abductive polynomial networks.
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    Inductive specification recovery: Understanding software by learning from example behaviors (1995)
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    Automated software engineering 2 (1995), S. 107-129 
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    ISSN: 1573-7535
    Keywords: induction ; machine learning ; reverse engineering ; Datalog
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We describe a technique for extracting specifications from software using machine learning techniques. In our proposed technique, instrumented code is run on a number of representative test cases, generating examples of its behavior. Inductive learning techniques are then used to generalize these examples, forming a general description of some aspect of the system's behavior. A case study is presented in which this “inductive specification recovery” method is used to find Datalog specifications forC code that implements database views, in the context of a large real-world software system. It is demonstrated that off-the-shelf inductive logic programming methods can be successfully used for specification recovery in this domain, but that these methods can be substantially improved by adapting them more closely to the task at hand.
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    Feature Based Modelling: A methodology for producing coherent, consistent, dynamically changing models of agents' competencies (1995)
    Springer
    User modeling and user adapted interaction 5 (1995), S. 117-150 
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    ISSN: 1573-1391
    Keywords: Student modelling ; machine learning ; modelling competency
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Feature Based Modelling uses attribute value machine learning techniques to model an agent's competency. This is achieved by creating a model describing the relationships between the features of the agent's actions and of the contexts in which those actions are performed. This paper describes techniques that have been developed for creating these models and for extracting key information therefrom. An overview is provided of previous studies that have evaluated the application of Feature Based Modelling in a number of educational contexts including piano keyboard playing, the unification of Prolog terms and elementary subtraction. These studies have demonstrated that the approach is applicable to a wide spectrum of domains. Classroom use has demonstrated the low computational overheads of the technique. A new study of the application of the approach to modelling elementary subtraction skills is presented. The approach demonstrates accuracy in excess of 90% when predicting student solutions. It also demonstrates the ability to identify and model student's buggy arithmetic procedures.
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    Toward Efficient Agnostic Learning (1994)
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    Machine learning 17 (1994), S. 115-141 
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    ISSN: 0885-6125
    Keywords: machine learning ; agnostic learning ; PAC learning ; computational learning theory
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In this paper we initiate an investigation of generalizations of the Probably Approximately Correct (PAC) learning model that attempt to significantly weaken the target function assumptions. The ultimate goal in this direction is informally termed agnostic learning, in which we make virtually no assumptions on the target function. The name derives from the fact that as designers of learning algorithms, we give up the belief that Nature (as represented by the target function) has a simple or succinct explanation. We give a number of positive and negative results that provide an initial outline of the possibilities for agnostic learning. Our results include hardness results for the most obvious generalization of the PAC model to an agnostic setting, an efficient and general agnostic learning method based on dynamic programming, relationships between loss functions for agnostic learning, and an algorithm for a learning problem that involves hidden variables.
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    Toward efficient agnostic learning (1994)
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    Machine learning 17 (1994), S. 115-141 
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    ISSN: 0885-6125
    Keywords: machine learning ; agnostic learning ; PAC learning ; computational learning theory
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In this paper we initiate an investigation of generalizations of the Probably Approximately Correct (PAC) learning model that attempt to significantly weaken the target function assumptions. The ultimate goal in this direction is informally termedagnostic learning, in which we make virtually no assumptions on the target function. The name derives from the fact that as designers of learning algorithms, we give up the belief that Nature (as represented by the target function) has a simple or succinct explanation. We give a number of positive and negative results that provide an initial outline of the possibilities for agnostic learning. Our results include hardness results for the most obvious generalization of the PAC model to an agnostic setting, an efficient and general agnostic learning method based on dynamic programming, relationships between loss functions for agnostic learning, and an algorithm for a learning problem that involves hidden variables.
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    URL: http://dx.doi.org/10.1007/BF00993468
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    An historical overview of natural language processing systems that learn (1994)
    Springer
    Artificial intelligence review 8 (1994), S. 17-54 
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    ISSN: 1573-7462
    Keywords: machine learning ; natural language processing ; cognitive modelling ; knowledge acquisition ; knowledge representation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A fundamental issue in natural language processing is the prerequisite of an enormous quantity of preprogrammed knowledge concerning both the language and the domain under examination. Manual acquisition of this knowledge is tedious and error prone. Development of an automated acquisition process would prove invaluable. This paper references and overviews a range of the systems that have been developed in the domain of machine learning and natural language processing. Each system is categorised into either a symbolic or connectionist paradigm, and has its own characteristics and limitations described.
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    URL: http://dx.doi.org/10.1007/BF00851349
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    Modelling the student in Pitagora 2.0 (1994)
    Springer
    User modeling and user adapted interaction 4 (1994), S. 233-251 
    Details
    ISSN: 1573-1391
    Keywords: Student modelling ; intelligent tutoring system ; machine learning ; explanation-based learning ; bayesian network ; experimental studies of construction and use of student models
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract With the aim to individualise human-computer interaction, an Intelligent Tutoring System (ITS) has to keep track of what and how the student has learned. Hence, it is necessary to maintain a Student Model (SM) dealing with complex knowledge representation, such as incomplete and inconsistent knowledge and belief revision. With this in view, the main objective of this paper is to present and discuss the student modelling approach we have adopted to implement Pitagora 2.0, an ITS based on a co-operative learning model, and designed to support teaching-learning activities in a Euclidean Geometry context. In particular, this approach has led us to develop two distinct modules that cooperate to implement the SM of Pitagora 2.0. The first module resembles a “classical” student model, in the sense that it maintains a representation of the current student knowledge level, which can be used by the teacher in order to tune its teaching strategies to the specific student needs. In addition, our system contains a second module that implements a virtual partner, called companion. This module consists of a computational model of an “average student” which cooperates with the student during the learning process. The above mentioned module calls for the use of machine learning algorithms that allow the companion to improve in parallel with the real student. Computational results obtained when testing this module in simulation experiments are also presented.
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    Heterogeneous learning in the Doppelgänger user modeling system (1994)
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    User modeling and user adapted interaction 4 (1994), S. 107-130 
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    ISSN: 1573-1391
    Keywords: User model ; machine learning ; server-client architecture ; multivariate statistical analysis ; Markov models ; Beta distribution ; linear prediction
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Doppelgänger is a generalized user modeling system that gathers data about users, performs inferences upon the data, and makes the resulting information available to applications.Doppelgänger's learning is calledheterogeneous for two reasons: first, multiple learning techniques are used to interpret the data, and second, the learning techniques must often grapple with disparate data types. These computations take place at geographically distributed sites, and make use of portable user models carried by individuals. This paper concentrates onDoppelgänger's learning techniques and their implementation in an application-independent, sensor-independent environment.
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    URL: http://dx.doi.org/10.1007/BF01099429
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    Machine Learning and the foundations of inductive inference (1993)
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    Minds and machines 3 (1993), S. 31-51 
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    ISSN: 1572-8641
    Keywords: Induction ; machine learning ; uniform convergence ; prior probability ; inductive logic
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract The problem of valid induction could be stated as follows: are we justified in accepting a given hypothesis on the basis of observations that frequently confirm it? The present paper argues that this question is relevant for the understanding of Machine Learning, but insufficient. Recent research in inductive reasoning has prompted another, more fundamental question: there is not just one given rule to be tested, there are a large number of possible rules, and many of these are somehow confirmed by the data — how are we to restrict the space of inductive hypotheses and choose effectively some rules that will probably perform well on future examples? We analyze if and how this problem is approached in standard accounts of induction and show the difficulties that are present. Finally, we suggest that the explanation-based learning approach and related methods of knowledge intensive induction could be, if not a solution, at least a tool for solving some of these problems.
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    URL: http://dx.doi.org/10.1007/BF00974304
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    A multi-layered learning model (1993)
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    Journal of intelligent and robotic systems 8 (1993), S. 399-423 
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    ISSN: 1573-0409
    Keywords: Meta-Learning ; concurrent learning ; opportunistic learning ; blackboard systems ; machine learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract The growth of technology is leading mankind to an increased awareness of the need for more intelligent systems. However, one of the bottlenecks in building intelligent systems is the difficulty of acquisition, testing and refinement of domain specialists' knowledge. Learning capability offers a way through this bottleneck. In this paper, we describe a general-purpose learning model for use in an unstructured environment. The proposed model exploits different learning techniques to improve the coordination, to increase task and resource allocation efficiency and to refine problem-solving skills of system elements. The utility of such system is most evident in complex domains such as ‘grasping unknown objects by a dextrous hand’. An example of the proposed model is illustrated by an intelligent dextrous hand which learns to grasp unknown objects. Moreover, an expert system for grasp mode selection was implemented in a software package and an example of grasp mode generation is demonstrated.
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    URL: http://dx.doi.org/10.1007/BF01257951
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    A knowledge-intensive genetic algorithm for supervised learning (1993)
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    Machine learning 13 (1993), S. 189-228 
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    ISSN: 0885-6125
    Keywords: Genetic algorithms ; machine learning ; symbolic learning ; supervised learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Supervised learning in attribute-based spaces is one of the most popular machine learning problems studied and, consequently, has attracted considerable attention of the genetic algorithm community. The fullmemory approach developed here uses the same high-level descriptive language that is used in rule-based systems. This allows for an easy utilization of inference rules of the well-known inductive learning methodology, which replace the traditional domain-independent operators and make the search task-specific. Moreover, a closer relationship between the underlying task and the processing mechanisms provides a setting for an application of more powerful task-specific heuristics. Initial results obtained with a prototype implementation for the simplest case of single concepts indicate that genetic algorithms can be effectively used to process high-level concepts and incorporate task-specific knowledge. The method of abstracting the genetic algorithm to the problem level, described here for the supervised inductive learning, can be also extended to other domains and tasks, since it provides a framework for combining recently popular genetic algorithm methods with traditional problem-solving methodologies. Moreover, in this particular case, it provides a very powerful tool enabling study of the widely accepted but not so well understood inductive learning methodology.
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    URL: http://dx.doi.org/10.1007/BF00993043
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    A Knowledge-Intensive Genetic Algorithm for Supervised Learning (1993)
    Springer
    Machine learning 13 (1993), S. 189-228 
    Details
    ISSN: 0885-6125
    Keywords: Genetic algorithms ; machine learning ; symbolic learning ; supervised learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Supervised learning in attribute-based spaces is one of the most popular machine learning problems studied and, consequently, has attracted considerable attention of the genetic algorithm community. The full-memory approach developed here uses the same high-level descriptive language that is used in rule-based systems. This allows for an easy utilization of inference rules of the well-known inductive learning methodology, which replace the traditional domain-independent operators and make the search task-specific. Moreover, a closer relationship between the underlying task and the processing mechanisms provides a setting for an application of more powerful task-specific heuristics. Initial results obtained with a prototype implementation for the simplest case of single concepts indicate that genetic algorithms can be effectively used to process high-level concepts and incorporate task-specific knowledge. The method of abstracting the genetic algorithm to the problem level, described here for the supervised inductive learning, can be also extended to other domains and tasks, since it provides a framework for combining recently popular genetic algorithm methods with traditional problem-solving methodologies. Moreover, in this particular case, it provides a very powerful tool enabling study of the widely accepted but not so well understood inductive learning methodology.
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    URL: http://dx.doi.org/10.1023/A:1022669929488
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    Adaptive generalisation (1993)
    Springer
    Artificial intelligence review 7 (1993), S. 313-328 
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    ISSN: 1573-7462
    Keywords: artificial neural nets ; transfer of training ; hyperplane method ; machine learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Adaptive generalisation is the ability to use prior knowledge in the performance of novel tasks. Thus, if we are to model intelligent behaviour with neural nets, they must be able to generalise across task domains. Our objective is to elucidate the aetiology of transfer of information between connectionist nets. First, a method is described that provides a standardised score for the quantification of how much task structure a net has extracted, and to what degree knowledge has been transferred between tasks. This method is then applied in three simulation studies to examine Input-to-Hidden (IH) and Hidden-to-Output (HO) decision hyperplanes as determinants of transfer effects. In the first study, positive transfer is demonstrated between functions that require the vertices of their spaces to be divided similarly, and negative transfer between functions that require decision regions of different shapes. In the other two studies, input and output similarity are varied independently in a series of paired associate learning tasks. Further explanation of transfer effects is provided through the use of a new technique that permits better visualisation of the entire computational space by showing both the relative position of inputs in Hidden Unit space, and the HO decision regions implemented by the set of weights.
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    Learning decision trees from decision rules: A method and initial results from a comparative study (1993)
    Springer
    Journal of intelligent information systems 2 (1993), S. 279-304 
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    ISSN: 1573-7675
    Keywords: machine learning ; inductive learning ; decision trees ; decision rules ; attribute selection
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A standard approach to determining decision trees is to learn them from examples. A disadvantage of this approach is that once a decision tree is learned, it is difficult to modify it to suit different decision making situations. Such problems arise, for example, when an attribute assigned to some node cannot be measured, or there is a significant change in the costs of measuring attributes or in the frequency distribution of events from different decision classes. An attractive approach to resolving this problem is to learn and store knowledge in the form of decision rules, and to generate from them, whenever needed, a decision tree that is most suitable in a given situation. An additional advantage of such an approach is that it facilitates buildingcompact decision trees, which can be much simpler than the logically equivalent conventional decision trees (by compact trees are meant decision trees that may contain branches assigned aset of values, and nodes assignedderived attributes, i.e., attributes that are logical or mathematical functions of the original ones). The paper describes an efficient method, AQDT-1, that takes decision rules generated by an AQ-type learning system (AQ15 or AQ17), and builds from them a decision tree optimizing a given optimality criterion. The method can work in two modes: thestandard mode, which produces conventional decision trees, andcompact mode, which produces compact decision trees. The preliminary experiments with AQDT-1 have shown that the decision trees generated by it from decision rules (conventional and compact) have outperformed those generated from examples by the well-known C4.5 program both in terms of their simplicity and their predictive accuracy.
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    URL: http://dx.doi.org/10.1007/BF00962072
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    Acquiring user models (1993)
    Springer
    Artificial intelligence review 7 (1993), S. 185-197 
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    ISSN: 1573-7462
    Keywords: user modeling ; user model acquisition ; machine learning ; stereotypes ; knowledge acquisition ; script-based expertise ; plan recognition
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Existing machine techniques for acquiring user models are characterized along five orthogonal dimensions: passive/active, user-initiated/automatic, logical/plausible, direct/indirect, and on-line/off-line. Passive techniques observe users whereas active techniques query users. User-initiated techniques require that users volunteer information; automatic techniques do not. The logical/plausible dimension measures the accuracy of derived user model data. Indirect techniques build upon data gathered by more direct methods. On-line techniques acquire user models in real-time during user interaction, while off-line techniques work after the user interaction is finished. Commonalities and differences in capabilities and features of different user model acquisition techniques are analyzed along the above dimensions, and the relationship of these techniques to similar techniques in other areas of artificial intelligence are discussed.
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    The role of memory and concepts in learning (1992)
    Springer
    Minds and machines 2 (1992), S. 239-265 
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    ISSN: 1572-8641
    Keywords: Representation ; cognitive architecture ; concepts ; machine learning ; game playing
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract The extent to which concepts, memory, and planning are necessary to the simulation of intelligent behavior is a fundamental philosophical issue in Artificial Intelligence. An active and productive segement of the AI community has taken the position that multiple low-level agents, properly organized, can account for high-level behavior. Empirical research on these questions with fully operational systems has been restricted to mobile robots that do simple tasks. This paper recounts experiments with Hoyle, a system in a cerebral, rather than a physical, domain. The program learns to perform well and quickly, often outpacing its human creators at two-person, perfect information board games. Hoyle demonstrates that a surprising amount of intelligent behavior can be treated as if it were situation-determined, that often planning is unnecessary, and that the memory required to support this learning is minimal. Concepts, however, are crucial to this reactive program's ability to learn and perform.
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    Problem representation for refinement (1992)
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    Minds and machines 2 (1992), S. 267-282 
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    ISSN: 1572-8641
    Keywords: Problem-solving ; strategy ; problem representation ; refinement ; machine learning ; mechanical discovery
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract In this paper we attempt to develop a problem representation technique which enables the decomposition of a problem into subproblems such that their solution in sequence constitutes a strategy for solving the problem. An important issue here is that the subproblems generated should be easier than the main problem. We propose to represent a set of problem states by a statement which is true for all the members of the set. A statement itself is just a set of atomic statements which are binary predicates on state variables. Then, the statement representing the set of goal states can be partitioned into its subsets each of which becomes a subgoal of the resulting strategy. The techniques involved in partitioning a goal into its subgoals are presented with examples.
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    Lower bound methods and separation results for on-line learning models (1992)
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    Machine learning 9 (1992), S. 107-145 
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    ISSN: 0885-6125
    Keywords: Formal models for learning ; learning algorithms ; lower bound arguments ; VC-dimension ; machine learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We consider the complexity of concept learning in various common models for on-line learning, focusing on methods for proving lower bounds to the learning complexity of a concept class. Among others, we consider the model for learning with equivalence and membership queries. For this model we give lower bounds on the number of queries that are needed to learn a concept classC in terms of the Vapnik-Chervonenkis dimension ofC, and in terms of the complexity of learningC with arbitrary equivalence queries. Furthermore, we survey other known lower bound methods and we exhibit all known relationships between learning complexities in the models considered and some relevant combinatorial parameters. As it turns out, the picture is almost complete. This paper has been written so that it can be read without previous knowledge of Computational Learning Theory.
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    A Bayesian Method for the Induction of Probabilistic Networks from Data (1992)
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    Machine learning 9 (1992), S. 309-347 
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    ISSN: 0885-6125
    Keywords: probabilistic networks ; Bayesian belief networks ; machine learning ; induction
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probabilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. Finally, we relate the methods in this paper to previous work, and we discuss open problems.
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    Lower Bound Methods and Separation Results for On-Line Learning Models (1992)
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    Machine learning 9 (1992), S. 107-145 
    Details
    ISSN: 0885-6125
    Keywords: Formal models for learning ; learning algorithms ; lower bound arguments ; VC-dimension ; machine learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We consider the complexity of concept learning in various common models for on-line learning, focusing on methods for proving lower bounds to the learning complexity of a concept class. Among others, we consider the model for learning with equivalence and membership queries. For this model we give lower bounds on the number of queries that are needed to learn a concept class $$\mathcal{C}$$ in terms of the Vapnik-Chervonenkis dimension of $$\mathcal{C}$$ , and in terms of the complexity of learning $$\mathcal{C}$$ with arbitrary equivalence queries. Furthermore, we survey other known lower bound methods and we exhibit all known relationships between learning complexities in the models considered and some relevant combinatorial parameters. As it turns out, the picture is almost complete. This paper has been written so that it can be read without previous knowledge of Computational Learning Theory.
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    A Bayesian method for the induction of probabilistic networks from data (1992)
    Springer
    Machine learning 9 (1992), S. 309-347 
    Details
    ISSN: 0885-6125
    Keywords: probabilistic networks ; Bayesian belief networks ; machine learning ; induction
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probabilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. Finally, we relate the methods in this paper to previous work, and we discuss open problems.
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    Explanation-Based Learning: A survey (1992)
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    Artificial intelligence review 6 (1992), S. 243-262 
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    ISSN: 1573-7462
    Keywords: Explanation-Based Learning ; Explanation-Based Generalization ; machine learning ; symbol-level learning ; failure-based systems ; real world applications
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The paper provides an introductory survey of Explanation-Based Learning (EBL). It attempts to define EBL's position in AI by exploring its relationship to other AI techniques, including other sub-fields of machine learning. Further issues discussed include the form of learning exhibited by EBL and potential applications of the method.
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    Integrated knowledge acquisition architectures (1992)
    Springer
    Journal of intelligent information systems 1 (1992), S. 9-34 
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    ISSN: 1573-7675
    Keywords: knowledge acquisition ; knowledge representation ; machine learning ; hypermedia
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract An architecture for knowledge acquisition systems is proposed based upon the integration of existing methodologies, techniques and tools which have been developed within the knowledge acquisition, machine learning, expert systems, hypermedia and knowledge representation research communities. Existing tools are analyzed within a common framework to show that their integration can be achieved in a natural and principled fashion. A system design is synthesized from what already exists, putting a diversity of well-founded and widely used approaches to knowledge acquisition within an integrative framework. The design is intended to be clean and simple, easy to understand, and easy to implement. A detailed architecture for integrated knowledge acquisition systems is proposed that also derives from parallel cognitive and theoretical studies.
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    Mining for knowledge in databases: The INLEN architecture, initial implementation and first results (1992)
    Springer
    Journal of intelligent information systems 1 (1992), S. 85-113 
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    ISSN: 1573-7675
    Keywords: knowledge discovery ; machine learning ; databases ; multistrategy systems
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The architecture of an intelligent multistrategy assistant for knowledge discovery from facts, INLEN, is described and illustrated by an exploratory application. INLEN integrates a database, a knowledge base, and machine learning methods within a uniform user-oriented framework. A variety of machine learning programs are incorporated into the system to serve as high-levelknowledge generation operators (KGOs). These operators can generate diverse kinds of knowledge about the properties and regularities existing in the data. For example, they can hypothesize general rules from facts, optimize the rules according to problem-dependent criteria, determine differences and similarities among groups of facts, propose new variables, create conceptual classifications, determine equations governing numeric variables and the conditions under which the equations apply, deriving statistical properties and using them for qualitative evaluations, etc. The initial implementation of the system, INLEN 1b, is described, and its performance is illustrated by applying it to a database of scientific publications.
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    The epistemology of conceptual clustering: KBG, an implementation (1992)
    Springer
    Journal of intelligent information systems 1 (1992), S. 57-84 
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    ISSN: 1573-7675
    Keywords: causality ; conceptual clustering ; confirmation ; growth of science ; induction ; knowledge representation ; machine learning ; numeric reasoning ; structuring knowledge ; symbolic reasoning ; uncertain reasoning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper analyzes differences between a numeric and symbolic approach to inductive inference. It shows the importance of existing structures in the acquisition of further knowledge, including statistical confirmation. We present a new way of looking at Hempel's paradox, in which both existing structures and statistical confirmation play a role in order to decrease the harm it does to learning. We point out some of the most important structures, and we illustrate how uncertainty does blur but does not destroy these structures. We conclude that pure symbolic as well as pure statistical learning is not realistic, but the integration of the two points of view is the key to future progress, but it is far from trivial. Our system KBG is a first-order logic conceptual clustering system; thus it builds knowledge structures out of unrelated examples. We describe the choices done in KBG in order to build these structures, using both numeric and symbolic types of knowledge. Our argument gives us firm grounds to contradict Carnap's view that induction is nothing but uncertain deduction, and to propose a refinement to Popper's “purely deductive” view of the growth of science. In our view, progressive organization of knowledge plays an essential role in the growth of new (inductive) scientific theories, that will be confirmed later, quite in the Popperian way.
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    URL: http://dx.doi.org/10.1007/BF01006414
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    Information-based evaluation criterion for classifier's performance (1991)
    Springer
    Machine learning 6 (1991), S. 67-80 
    Details
    ISSN: 0885-6125
    Keywords: Classifier ; evaluation criteria ; machine learning ; information theory
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In the past few years many systems for learning decision rules from examples were developed. As different systems allow different types of answers when classifying new instances, it is difficult to appropriately evaluate the systems' classification power in comparison with other classification systems or in comparison with human experts. Classification accuracy is usually used as a measure of classification performance. This measure is, however, known to have several defects. A fair evaluation criterion should exclude the influence of the class probabilities which may enable a completely uninformed classifier to trivially achieve high classification accuracy. In this paper a method for evaluating the information score of a classifier's answers is proposed. It excludes the influence of prior probabilities, deals with various types of imperfect or probabilistic answers and can be used also for comparing the performance in different domains.
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    URL: http://dx.doi.org/10.1007/BF00153760
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    Information-Based Evaluation Criterion for Classifier's Performance (1991)
    Springer
    Machine learning 6 (1991), S. 67-80 
    Details
    ISSN: 0885-6125
    Keywords: Classifier ; evaluation criteria ; machine learning ; information theory
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In the past few years many systems for learning decision rules from examples were developed. As different systems allow different types of answers when classifying new instances, it is difficult to appropriately evaluate the systems' classification power in comparison with other classification systems or in comparison with human experts. Classification accuracy is usually used as a measure of classification performance. This measure is, however, known to have several defects. A fair evaluation criterion should exclude the influence of the class probabilities which may enable a completely uninformed classifier to trivially achieve high classification accuracy. In this paper a method for evaluating the information score of a classifier's answers is proposed. It excludes the influence of prior probabilities, deals with various types of imperfect or probabilistic answers and can be used also for comparing the performance in different domains.
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    URL: http://dx.doi.org/10.1023/A:1022642017308
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  • 88
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    Knowledge base refinement: A bibliography (1991)
    Springer
    Applied intelligence 1 (1991), S. 87-94 
    Details
    ISSN: 1573-7497
    Keywords: Knowledge base refinement ; automatic knowledge acquisition ; machine learning ; reasoning under uncertainty
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Knowledge base refinement is a learning process aimed at adjusting a knowledge base for the purpose of improving the breadth, accuracy, efficiency, and efficacy of the associated knowledge-based system(s). This annotated bibliography gives an overview of this emerging field.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00117748
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  • 89
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    Using single-layered neural networks for the extraction of conjunctive rules and hierarchical classifications (1991)
    Springer
    Applied intelligence 1 (1991), S. 157-173 
    Details
    ISSN: 1573-7497
    Keywords: neural networks ; machine learning ; knowledge acquisition
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Machine Learning is an area concerned with the automation of the process of knowledge acquisition. Neural networks generally represent their knowledge at the lower level, while knowledge based systems use higher level knowledge representations. The method we propose here, provides a technique which automatically allows us to extract production rules from the lower level representation used by a single-layered neural networks trained by Hebb's rule. Even though a single-layered neural network can not model complex, nonlinear domains, their strength in dealing with noise has enabled us to produce correct rules in a noisy domain.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00058881
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  • 90
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    An AI approach for wastewater treatment systems (1991)
    Springer
    Applied intelligence 1 (1991), S. 247-261 
    Details
    ISSN: 1573-7497
    Keywords: Wastewater treatment ; machine learning ; synthesis ; heuristic search ; Hopfield network
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Most wastewaters consist of several contaminants (compounds) that need to be removed during the treatment process. A treatability database has been developed containing the treatability of various compounds through different types of treatment processes. In most wastewaters several compounds appear together and two or more treatment processes in series may be needed to meet the effluent limits of the contaminants. The proposed AI wastewater treatment system consists of two phases, analysis phase and synthesis phase. In the analysis phase, an inductive learning algorithm with a grammar based knowledge representation is used to extract knowledge rules from the database. These rules are combined with another set of rules obtained from the experts. All these rules are arranged together to identify the effect of an individual treatment process on several compounds at various concentrations. In the synthesis phase, knowledge rules generated from the analysis phase are used to obtain the sequence of technologies that can satisfy the necessary treatment constraints. Two different methodologies are developed to generate the sequence of technologies. In the first approach, the synthesis phase is formulated as a search problem and a heuristic search function is developed. In the second approach, the synthesis phase is formulated as an optimization problem and a Hopfield neural network is used to obtain the sequence of technologies. Both approaches are compared for the optimality of the solution and the processing time required.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00118999
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  • 91
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    Enhancing medical expert systems with knowledge obtained from statistical data (1990)
    Springer
    Annals of mathematics and artificial intelligence 2 (1990), S. 261-276 
    Details
    ISSN: 1573-7470
    Keywords: Automated medical diagnostic systems ; weights of evidence ; machine learning ; logistic regression
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract This paper describes the progress to date on the development of a medical diagnostic system for veterinary medicine. The system is being designed to compare and combine methodologies ranging from an implementation of the doctor's decision making protocols to classical statistical, machine learning and evidence combination schemes. The application of these methods to the domain of equine colic are discussed and initial results summarized.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01531011
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  • 92
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    Interactive and automatic image recognition system (1988)
    Springer
    Machine vision and applications 1 (1988), S. 59-69 
    Details
    ISSN: 1432-1769
    Keywords: image processing ; machine learning ; matching ; modeling ; object recognition
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We have developed a vision system which learns to recognize many kinds of two-dimensional objects in many kinds of images. Image processing program modules are classified based on functions in the library. First, the user can teach the system the way to recognize objects in the image interactively testing the effectiveness of each program by trial and error. The system stores what it learns in the long-term memory calledmodel. The model is improved by analyzing training images in the same way. Once the model is completed, the system can automatically analyze images in the same category and recognize the expected objects in a top-down way driven by the model. Since a model is built for images in each category, the system can recognize various kinds of images simply by retrieving the corresponding models.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01212312
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  • 93
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    Explicit representation of terms defined by counter examples (1987)
    Springer
    Journal of automated reasoning 3 (1987), S. 301-317 
    Details
    ISSN: 1573-0670
    Keywords: Herbrand universe ; counter examples ; learning disjunctive concepts ; machine learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Anti-unification guarantees the existence of a term which is an explicit representation of the most specific generalization of a collection of terms. This provides a formal basis for learning from examples. Here we address the dual problem of computing a generalization given a set of counter examples. Unlike learning from examples an explicit, finite representation for the generalization does not always exist. We show that the problem is decidable by providing an algorithm which, given an implicit representation will return a finite explicit representation or report that none exists. Applications of this result to the problem of negation as failure and to the representation of solutions to systems of equations and inequations are also mentioned.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00243794
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    Explanation-Based Learning: An Alternative View (1986)
    Springer
    Machine learning 1 (1986), S. 145-176 
    Details
    ISSN: 0885-6125
    Keywords: machine learning ; concept acquisition ; explanation-based learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In the last issue of this journal Mitchell, Keller, and Kedar-Cabelli presented a unifying framework for the explanation-based approach to machine learning. While it works well for a number of systems, the framework does not adequately capture certain aspects of the systems under development by the explanation-based learning group at Illinois. The primary inadequacies arise in the treatment of concept operationality, organization of knowledge into schemata, and learning from observation. This paper outlines six specific problems with the previously proposed framework and presents an alternative generalization method to perform explanation-based learning of new concepts.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1022898111663
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  • 95
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    Explanation-based learning: An alternative view (1986)
    Springer
    Machine learning 1 (1986), S. 145-176 
    Details
    ISSN: 0885-6125
    Keywords: machine learning ; concept acquisition ; explanation-based learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In the last issue of this journal Mitchell, Keller, and Kedar-Cabelli presented a unifying framework for the explanation-based approach to machine learning. While it works well for a number of systems, the framework does not adequately capture certain aspects of the systems under development by the explanation-based learning group at Illinois. The primary inadequacies arise in the treatment of concept operationality, organization of knowledge into schemata, and learning from observation. This paper outlines six specific problems with the previously proposed framework and presents an alternative generalization method to perform explanation-based learning of new concepts.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00114116
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    Computer diagnosis of goiters. V. The input (1977)
    Springer
    International journal of parallel programming 6 (1977), S. 237-261 
    Details
    ISSN: 1573-7640
    Keywords: Computer diagnosis ; pattern recognition ; thyroid pathology ; observation error ; classification error ; reclassification ; Bayesian inference ; Parzen window ; sequential feature extraction ; error correction ; machine learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A scheme in which model construction and operation are considered as distinct processes has been designed for the differential diagnosis of goiters. The influence of classification and observation errors and of the recognition method on the diagnostic accuracy has been determined.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01002334
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