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  • Articles: DFG German National Licenses  (392)
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  • Articles  (392)
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  • 1995-1999  (354)
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  • 1
    Electronic Resource
    Electronic Resource
    Stability of non-Markovian polling systems (1995)
    Springer
    Queueing systems 21 (1995), S. 67-95 
    Details
    ISSN: 1572-9443
    Keywords: Polling systems ; stability ; stationary regime
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A stationary regime for polling systems with general ergodic (G/G) arrival processes at each station is constructed. Mutual independence of the arrival processes is not required. It is shown that the stationary workload so constructed is minimal in the stochastic ordering sense. In the model considered the server switches from station to station in a Markovian fashion, and a specific service policy is applied to each queue. Our hypotheses cover the purely gated, thea-limited, the binomial-gated and other policies. As a by-product we obtain sufficient conditions for the stationary regime of aG/G/1/∞ queue with multiple server vacations (see Doshi [11]) to be ergodic.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01158575
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  • 2
    Electronic Resource
    Electronic Resource
    A stability criterion via fluid limits and its application to a polling system (1999)
    Springer
    Queueing systems 32 (1999), S. 131-168 
    Details
    ISSN: 1572-9443
    Keywords: stability ; positive recurrence ; fluid limit ; polling system ; exhaustive service policy
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We introduce a generalized criterion for the stability of Markovian queueing systems in terms of stochastic fluid limits. We consider an example in which this criterion may be applied: a polling system with two stations and two heterogeneous servers.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1019187004209
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  • 3
    Electronic Resource
    Electronic Resource
    The Role of Statistics in IS/IT: Practical Gains from Mined Data (1999)
    Springer
    Information systems frontiers 1 (1999), S. 259-266 
    Details
    ISSN: 1572-9419
    Keywords: data mining ; statistics ; patterns in data ; fitting distributions ; lambda ; beta
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Data mining has, in the past, tended to use simplistic statistical methods (or even none at all). In this paper we show by example how cutting edge (but easy to use and comprehend) statistical methods can yield substantial gains in data mining. The role of statistics in IS/IT (information systems and information technology) in general can be substantial, yielding more nearly optimal performance of problems at the emerging frontiers in all their aspects.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1010002428579
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  • 4
    Electronic Resource
    Electronic Resource
    Piecewise linear test functions for stability and instability of queueing networks (1997)
    Springer
    Queueing systems 27 (1997), S. 205-226 
    Details
    ISSN: 1572-9443
    Keywords: multiclass queueing networks ; ergodicity ; stability ; performance analysis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We develop the use of piecewise linear test functions for the analysis of stability of multiclass queueing networks and their associated fluid limit models. It is found that if an associated LP admits a positive solution, then a Lyapunov function exists. This implies that the fluid limit model is stable and hence that the network model is positive Harris recurrent with a finite polynomial moment. Also, it is found that if a particular LP admits a solution, then the network model is transient.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1019166115653
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  • 5
    Electronic Resource
    Electronic Resource
    Stability of a three‐station fluid network (1999)
    Springer
    Queueing systems 33 (1999), S. 293-325 
    Details
    ISSN: 1572-9443
    Keywords: stability ; fluid models ; multiclass queueing networks ; piecewise linear Lyapunov functions ; linear Lyapunov functions ; monotone global stability ; static buffer priority disciplines
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper studies the stability of a three‐station fluid network. We show that, unlike the two‐station networks in Dai and Vande Vate [18], the global stability region of our three‐station network is not the intersection of its stability regions under static buffer priority disciplines. Thus, the “worst” or extremal disciplines are not static buffer priority disciplines. We also prove that the global stability region of our three‐station network is not monotone in the service times and so, we may move a service time vector out of the global stability region by reducing the service time for a class. We introduce the monotone global stability region and show that a linear program (LP) related to a piecewise linear Lyapunov function characterizes this largest monotone subset of the global stability region for our three‐station network. We also show that the LP proposed by Bertsimas et al. [1] does not characterize either the global stability region or even the monotone global stability region of our three‐station network. Further, we demonstrate that the LP related to the linear Lyapunov function proposed by Chen and Zhang [11] does not characterize the stability region of our three‐station network under a static buffer priority discipline.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1019184331042
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  • 6
    Electronic Resource
    Electronic Resource
    Dynamic scheduling in multiclass queueing networks: Stability under discrete-review policies (1999)
    Springer
    Queueing systems 31 (1999), S. 171-206 
    Details
    ISSN: 1572-9443
    Keywords: scheduling ; open multiclass queueing networks ; discrete-review policies ; fluid models ; stability
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper describes a family of discrete-review policies for scheduling open multiclass queueing networks. Each of the policies in the family is derived from what we call a dynamic reward function: such a function associates with each queue length vector q and each job class k a positive value r k (q), which is treated as a reward rate for time devoted to processing class k jobs. Assuming that each station has a traffic intensity parameter less than one, all policies in the family considered are shown to be stable. In such a policy, system status is reviewed at discrete points in time, and at each such point the controller formulates a processing plan for the next review period, based on the queue length vector observed. Stability is proved by combining elementary large deviations theory with an analysis of an associated fluid control problem. These results are extended to systems with class dependent setup times as well as systems with alternate routing and admission control capabilities.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1019106213778
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  • 7
    Electronic Resource
    Electronic Resource
    On the stability of retrial queues (1997)
    Springer
    Queueing systems 26 (1997), S. 343-363 
    Details
    ISSN: 1572-9443
    Keywords: retrial queues ; stability ; ergodicity ; renovation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We consider the following Type of problems. Calls arrive at a queue of capacity K (which is called the primary queue), and attempt to get served by a single server. If upon arrival, the queue is full and the server is busy, the new arriving call moves into an infinite capacity orbit, from which it makes new attempts to reach the primary queue, until it finds it non-full (or it finds the server idle). If the queue is not full upon arrival, then the call (customer) waits in line, and will be served according to the FIFO order. If λ is the arrival rate (average number per time unit) of calls and μ is one over the expected service time in the facility, it is well known that μ 〉 λ is not always sufficient for stability. The aim of this paper is to provide general conditions under which it is a sufficient condition. In particular, (i) we derive conditions for Harris ergodicity and obtain bounds for the rate of convergence to the steady state and large deviations results, in the case that the inter-arrival times, retrial times and service times are independent i.i.d. sequences and the retrial times are exponentially distributed; (ii) we establish conditions for strong coupling convergence to a stationary regime when either service times are general stationary ergodic (no independence assumption), and inter-arrival and retrial times are i.i.d. exponentially distributed; or when inter-arrival times are general stationary ergodic, and service and retrial times are i.i.d. exponentially distributed; (iii) we obtain conditions for the existence of uniform exponential bounds of the queue length process under some rather broad conditions on the retrial process. We finally present conditions for boundedness in distribution for the case of nonpatient (or non persistent) customers.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1019193527040
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  • 8
    Electronic Resource
    Electronic Resource
    Recurrence and transience properties of some neural networks: an approach via fluid limit models (1999)
    Springer
    Queueing systems 32 (1999), S. 99-130 
    Details
    ISSN: 1572-9443
    Keywords: neural network ; inhibition ; stability ; Markov process ; fluid limit ; Harris-recurrence ; transience
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The subject of the paper is the stability analysis of some neural networks consisting of a finite number of interacting neurons. Following the approach of Dai [5] we use the fluid limit model of the network to derive a sufficient condition for positive Harris-recurrence of the associated Markov process. This improves the main result in Karpelevich et al. [11] and, at the same time, sheds some new light on it. We further derive two different conditions that are sufficient for transience of the state process and illustrate our results by classifying some examples according to positive recurrence or transience.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1019135020138
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  • 9
    Electronic Resource
    Electronic Resource
    Window flow control in FIFO networks with cross traffic (1999)
    Springer
    Queueing systems 32 (1999), S. 195-231 
    Details
    ISSN: 1572-9443
    Keywords: window flow control ; TCP ; stability ; multiclass networks ; stationary ergodic point processes ; (max,+)-linear system
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We focus on window flow control as used in packet-switched communication networks. The approach consists in studying the stability of a system where each node on the path followed by the packets of the controlled connection is modeled by a FIFO (First-In-First-Out) queue of infinite capacity which receives in addition some cross traffic represented by an exogenous flow. Under general stochastic assumptions, namely for stationary and ergodic input processes, we show the existence of a maximum throughput allowed by the flow control. Then we establish bounds on the value of this maximum throughput. These bounds, which do not coincide in general, are reached by time-space scalings of the exogenous flows. Therefore, the performance of the window flow control depends not only on the traffic intensity of the cross flows, but also on fine statistical characteristics such as the burstiness of these flows. These results are illustrated by several examples, including the case of a nonmonotone, nonconvex and fractal stability region.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1019191105117
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  • 10
    Electronic Resource
    Electronic Resource
    First Order Regression (1997)
    Springer
    Machine learning 26 (1997), S. 147-176 
    Details
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1007365207130
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  • 11
    Electronic Resource
    Electronic Resource
    Learning Qualitative Models of Dynamic Systems (1997)
    Springer
    Machine learning 26 (1997), S. 177-211 
    Details
    ISSN: 0885-6125
    Keywords: inductive logic programming ; qualitative modelling ; system identification ; PAC learning ; physiological modelling ; cardiovascular system ; data mining ; patient monitoring
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The automated construction of dynamic system models is an important application area for ILP. We describe a method that learns qualitative models from time-varying physiological signals. The goal is to understand the complexity of the learning task when faced with numerical data, what signal processing techniques are required, and how this affects learning. The qualitative representation is based on Kuipers' QSIM. The learning algorithm for model construction is based on Coiera's GENMODEL. We show that QSIM models are efficiently PAC learnable from positive examples only, and that GENMODEL is an ILP algorithm for efficiently constructing a QSIM model. We describe both GENMOEL which performs RLGG on qualitative states to learn a QSIM model, and the front-end processing and segmenting stages that transform a signal into a set of qualitative states. Next we describe results of experiments on data from six cardiac bypass patients. Useful models were obtained, representing both normal and abnormal physiological states. Model variation across time and across different levels of temporal abstraction and fault tolerance is explored. The assumption made by many previous workers that the abstraction of examples from data can be separated from the learning task is not supported by this study. Firstly, the effects of noise in the numerical data manifest themselves in the qualitative examples. Secondly, the models learned are directly dependent on the initial qualitative abstraction chosen.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1007317323969
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  • 12
    Electronic Resource
    Electronic Resource
    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 
    Details
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1000904004065
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  • 13
    Electronic Resource
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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 
    Details
    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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  • 14
    Electronic Resource
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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 
    Details
    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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  • 15
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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 
    Details
    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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  • 16
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    Extracting knowledge from examples: Induction of heuristic rules for wheelchair prescription (1995)
    Springer
    Journal of intelligent and robotic systems 14 (1995), S. 133-153 
    Details
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01559609
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  • 17
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    Stable Sampled-data Adaptive Control of Robot Arms Using Neural Networks (1997)
    Springer
    Journal of intelligent and robotic systems 20 (1997), S. 131-155 
    Details
    ISSN: 1573-0409
    Keywords: robot adaptive control ; basis function-like networks ; stability ; discrete variable structure
    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 Stable neural network-based sampled-data indirect and direct adaptivecontrol approaches, which are the integration of a neural network (NN)approach and the adaptive implementation of the discrete variable structurecontrol, are developed in this paper for the trajectory tracking control ofa robot arm with unknown nonlinear dynamics. The robot arm is assumed tohave an upper and lower bound of its inertia matrix norm and its states areavailable for measurement. The discrete variable structure control servestwo purposes, i.e., one is to force the system states to be within the stateregion in which neural networks are used when the system goes out of neuralcontrol; and the other is to improve the tracking performance within the NNapproximation region. Main theory results for designing stable neuralnetwork-based sampled data indirect and direct adaptive controllers aregiven, and the extension of the proposed control approaches to the compositeadaptive control of a flexible-link robot is discussed. Finally, theeffectiveness of the proposed control approaches is illustrated throughsimulation studies.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1007900125801
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  • 18
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    Forward Assembly Planning Based on Stability (1997)
    Springer
    Journal of intelligent and robotic systems 19 (1997), S. 411-436 
    Details
    ISSN: 1573-0409
    Keywords: assembly planning ; stability ; robot ; forward ; operations
    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 paper presents an approach to sequence planning consisting in determining assembly sequences defined in terms of mating and non-mating operations and based on a dynamic expansion of the assembly tree obtained using a knowledge base management system. The planner considers the case of a single-robot assembly workcell. The use of stability and the detailed definition of sequences also by means of several non-mating operations are shown to be powerful instruments in the control of the tree expansion. Forward assembly planning has been chosen, in order to minimize the number of stability checks. Backtracking is avoided by combining precedence relations and stability analysis. Hard and soft constrains are introduced to drive the tree expansion. Hard constraints are precedence relations and stability analysis. All operations are associated to costs, which are used as soft constraints. The operation based approach enables one to manage even non-mating operations and to easily overcome the linearity constraint. Costs enable the planner to manage the association among tools and components. The first section of the paper concerns Stability Analysis that is subdivided into Static and Dynamic Stability Analysis. The former is mainly involved in analyzing gravity effects; the latter is mainly involved in evaluate inertia effects due to manipulation. Stability Analysis is implemented in a simplified form. Fundamental assumptions are: no rotational equilibrium condition is considered; for each reaction force only direction and versus, but not magnitude, are considered; friction is neglected. The second section discusses the structure of the planner and its implementation. The planner is a rule based system. Forward chaining and hypothetical reasoning are the inference strategies used. The knowledge base and the data base of the system are presented and the advantages obtained using a rule based system are discussed. The third section shows two planning examples, showing the performance of the system in a simple case and in an industrial test case, the assembly of a microwave branching filter composed of 26 components.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1007928631050
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  • 19
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    Stable Neuro-Adaptive Control for Robots with the Upper Bound Estimation on the Neural Approximation Errors (1999)
    Springer
    Journal of intelligent and robotic systems 26 (1999), S. 91-100 
    Details
    ISSN: 1573-0409
    Keywords: robots ; neural networks ; adaptiveness ; stability ; approximation
    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 An indirect adaptive control approach is developed in this paper for robots with unknown nonlinear dynamics using neural networks (NNs). A key property of the proposed approach is that the actual joint angle values in the control law are replaced by the desired joint angles, angle velocities and accelerators, and the bound on the NN reconstruction errors is assumed to be unknown. Main theoretical results for designing such a neuro-controller are given, and the control performance of the proposed controller is verified with simulation studies.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008195720685
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  • 20
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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 
    Details
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1007960328527
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  • 21
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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 
    Details
    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.
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    URL: http://dx.doi.org/10.1023/A:1008012519312
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    Classification Accuracy: Machine Learning vs. Explicit Knowledge Acquisition (1995)
    Springer
    Machine learning 18 (1995), S. 109-114 
    Details
    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)
    Springer
    Machine learning 18 (1995), S. 109-114 
    Details
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00993823
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    Learning from a Population of Hypotheses (1995)
    Springer
    Machine learning 18 (1995), S. 255-276 
    Details
    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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    URL: http://dx.doi.org/10.1023/A:1022855530995
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    Learning from a population of hypotheses (1995)
    Springer
    Machine learning 18 (1995), S. 255-276 
    Details
    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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    URL: http://dx.doi.org/10.1007/BF00993412
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    Technical note: Bias and the quantification of stability (1995)
    Springer
    Machine learning 20 (1995), S. 23-33 
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    ISSN: 0885-6125
    Keywords: stability ; bias ; accuracy ; repeatability ; agreement ; similarity
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Research on bias in machine learning algorithms has generally been concerned with the impact of bias on predictive accuracy. We believe that there are other factors that should also play a role in the evaluation of bias. One such factor is the stability of the algorithm; in other words, the repeatability of the results. If we obtain two sets of data from the same phenomenon, with the same underlying probability distribution, then we would like our learning algorithm to induce approximately the same concepts from both sets of data. This paper introduces a method for quantifying stability, based on a measure of the agreement between concepts. We also discuss the relationships among stability, predictive accuracy, and bias.
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    URL: http://dx.doi.org/10.1007/BF00993473
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    Technical Note: Bias and the Quantification of Stability (1995)
    Springer
    Machine learning 20 (1995), S. 23-33 
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    ISSN: 0885-6125
    Keywords: stability ; bias ; accuracy ; repeatability ; agreement ; similarity
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Research on bias in machine learning algorithms has generally been concerned with the impact of bias on predictive accuracy. We believe that there are other factors that should also play a role in the evaluation of bias. One such factor is the stability of the algorithm; in other words, the repeatability of the results. If we obtain two sets of data from the same phenomenon, with the same underlying probability distribution, then we would like our learning algorithm to induce approximately the same concepts from both sets of data. This paper introduces a method for quantifying stability, based on a measure of the agreement between concepts. We also discuss the relationships among stability, predictive accuracy, and bias.
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    URL: http://dx.doi.org/10.1023/A:1022682001417
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    Emergence of symmetric, modular, and reciprocal connections in recurrent networks with Hebbian learning (1999)
    Springer
    Biological cybernetics 81 (1999), S. 211-225 
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    ISSN: 1432-0770
    Keywords: Key words: Hebbian learning rule ; attractor dynamics ; symmetric connections ; multiplicative normalization ; self-organization ; stability
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract. While learning and development are well characterized in feedforward networks, these features are more difficult to analyze in recurrent networks due to the increased complexity of dual dynamics – the rapid dynamics arising from activation states and the slow dynamics arising from learning or developmental plasticity. We present analytical and numerical results that consider dual dynamics in a recurrent network undergoing Hebbian learning with either constant weight decay or weight normalization. Starting from initially random connections, the recurrent network develops symmetric or near-symmetric connections through Hebbian learning. Reciprocity and modularity arise naturally through correlations in the activation states. Additionally, weight normalization may be better than constant weight decay for the development of multiple attractor states that allow a diverse representation of the inputs. These results suggest a natural mechanism by which synaptic plasticity in recurrent networks such as cortical and brainstem premotor circuits could enhance neural computation and the generation of motor programs.
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    URL: http://dx.doi.org/10.1007/s004220050557
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    Stability analysis of a general toeplitz systems solver (1995)
    Springer
    Numerical algorithms 10 (1995), S. 225-244 
    Details
    ISSN: 1572-9265
    Keywords: Cholesky factorization error analysis ; Hankel matrix ; least squares ; normal equations ; orthogonal factorization ; QR factorization ; semi-normal equations ; stability ; Toeplitz matrix ; weak stability ; Primary 65F25 ; Secondary 47B35 ; 65F05 ; 65F30 ; 65Y05 ; 65Y10
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We show that a fast algorithm for theQR factorization of a Toeplitz or Hankel matrixA is weakly stable in the sense thatR T R is close toA T A. Thus, when the algorithm is used to solve the semi-normal equationsR TRx=AT b, we obtain a weakly stable method for the solution of a nonsingular Toeplitz or Hankel linear systemAx=b. The algorithm also applies to the solution of the full-rank Toeplitz or Hankel least squares problem min ||Ax-b||2.
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    Progressive stable interpolation (1997)
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    Numerical algorithms 14 (1997), S. 343-359 
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    ISSN: 1572-9265
    Keywords: progressive interpolation ; stability ; spline ; shape parameters ; geometric continuity ; 41A05 ; 41A15 ; 65D05 ; 65D07
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract In this paper, we study several interpolating and smoothing methods for data which are known “progressively”. The algorithms proposed are governed by recurrence relations and our principal goal is to study their stability. A recurrence relation will be said stable if the spectral radius of the associated matrix is less than one. The iteration matrices depend on shape parameters which come either from the connection at the knots, or from the nature of the interpolant between two knots. We obtain various stability domains. Moving the parameters inside these domains leads to interesting shape effects.
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    URL: http://dx.doi.org/10.1023/A:1019129400864
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    Multistep methods for differential algebraic equations (1995)
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    Numerical algorithms 10 (1995), S. 245-260 
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    ISSN: 1572-9265
    Keywords: Multistep methods ; differential-algebraic equations ; stability ; existence and uniqueness ; convergence of iterative method ; 65L06 ; 65L20 ; 65N22
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Multistep methods for the differential/algebraic equations (DAEs) in the form of $$F_1 (x) = 0, F_2 (x,x',z) = 0$$ are presented, whereF 1 maps from ℝ n to ℝ ′ ,F 2 from ℝ n x ℝ n x ℝ m to ℝ s andr〈n≤r+s=n+m. By employing the deviations of the available existence theories, a new form of the multistep method for solutions of (1) is developed. Furthermore, it is shown that this method has no typical instabilities such as those that may occur in the application of multistep method to DAEs in the traditional manner. A proof of the solvability of the multistep system is provided, and an iterative method is developed for solving these nonlinear algebraic equations. Moreover, a proof of the convergence of this iterative method is presented.
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    A Search for Hidden Relationships: Data Mining with Genetic Algorithms (1997)
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    Computational economics 10 (1997), S. 267-277 
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    ISSN: 1572-9974
    Keywords: data mining ; forecasting ; genetic algorithms.
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Economics
    Notes: Abstract This paper presents an algorithm that permits the search for dependencies among sets of data (univariate or multivariate time-series, or cross-sectional observations). The procedure is modeled after genetic theories and Darwinian concepts, such as natural selection and survival of the fittest. It permits the discovery of equations of the data-generating process in symbolic form. The genetic algorithm that is described here uses parts of equations as building blocks to breed ever better formulas. Apart from furnishing a deeper understanding of the dynamics of a process, the method also permits global predictions and forecasts. The algorithm is successfully tested with artificial and with economic time-series and also with cross-sectional data on the performance and salaries of NBA players during the 94–95 season.
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    URL: http://dx.doi.org/10.1023/A:1008673309609
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    Websom for Textual Data Mining (1999)
    Springer
    Artificial intelligence review 13 (1999), S. 345-364 
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    ISSN: 1573-7462
    Keywords: data mining ; document filtering ; exploratory data analysis ; information retrieval ; self-organizing map ; SOM ; text document collection ; WEBSOM
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract New methods that are user-friendly and efficient are needed for guidanceamong the masses of textual information available in the Internet and theWorld Wide Web. We have developed a method and a tool called the WEBSOMwhich utilizes the self-organizing map algorithm (SOM) for organizing largecollections of text documents onto visual document maps. The approach toprocessing text is statistically oriented, computationally feasible, andscalable – over a million text documents have been ordered on a single map.In the article we consider different kinds of information needs and tasksregarding organizing, visualizing, searching, categorizing and filteringtextual data. Furthermore, we discuss and illustrate with examples howdocument maps can aid in these situations. An example is presented wherea document map is utilized as a tool for visualizing and filtering a stream ofincoming electronic mail messages.
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    Learning, goals, and learning goals: A perspective on goal-driven learning (1995)
    Springer
    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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    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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    Comparing Structures Using a Hopfield-Style Neural Network (1999)
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    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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    Efficient GA Based Techniques for Classification (1999)
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    Applied intelligence 11 (1999), S. 277-284 
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    ISSN: 1573-7497
    Keywords: genetic algorithms ; classification ; data mining
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A common approach to evaluating competing models in a classification context is via accuracy on a test set or on cross-validation sets. However, this can be computationally costly when using genetic algorithms with large datasets and the benefits of performing a wide search are compromised by the fact that estimates of the generalization abilities of competing models are subject to noise. This paper shows that clear advantages can be gained by using samples of the test set when evaluating competing models. Further, that applying statistical tests in combination with Occam's razor produces parsimonious models, matches the level of evaluation to the state of the search and retains the speed advantages of test set sampling.
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    Discretisation of Continuous Commercial Database Features for a Simulated Annealing Data Mining Algorithm (1999)
    Springer
    Applied intelligence 11 (1999), S. 285-295 
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    ISSN: 1573-7497
    Keywords: discretisation ; data mining ; simulated annealing
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract An introduction to the approaches used to discretise continuous database features is given, together with a discussion of the potential benefits of such techniques. These benefits are investigated by applying discretisation algorithms to two large commercial databases; the discretisations yielded are then evaluated using a simulated annealing based data mining algorithm. The results produced suggest that dramatic reductions in problem size may be achieved, yielding improvements in the speed of the data mining algorithm. However, it is also demonstrated under certain circumstances that the discretisation produced may give an increase in problem size or allow overfitting by the data mining algorithm. Such cases, within which often only a small proportion of the database belongs to the class of interest, highlight the need both for caution when producing discretisations and for the development of more robust discretisation algorithms.
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    Characteristic Rule Discovery in Aurum-3 (1999)
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    Applied intelligence 11 (1999), S. 297-304 
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    ISSN: 1573-7497
    Keywords: data mining ; rule discovery ; interest measure ; distinctive features ; characteristic rules
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract One strategy for increasing the efficiency of rule discovery in data mining is to target a restricted class of rules, such as exact or almost exact rules, rules with a limited number of conditions, or rules in which each condition, on its own, eliminates a competing outcome class. An algorithm is presented for the discovery of rules in which each condition is a distinctive feature of the outcome class on its right-hand side in the subset of the data set defined by the conditions, if any, which precede it. Such a rule is said to be characteristic for the outcome class. A feature is defined as distinctive for an outcome class if it maximises a well-known measure of rule interest or is unique to the outcome class in the data set. In the special case of data mining which arises when each outcome class is represented by a single instance in the data set, a feature of an object is shown to be distinctive if and only if no other feature is shared by fewer objects in the data set.
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    Imputation of Missing Data in Industrial Databases (1999)
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    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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    Exploiting Hierarchy in Text Categorization (1999)
    Springer
    Information retrieval 1 (1999), S. 193-216 
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    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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    Discovering dynamics: From inductive logic programming to machine discovery (1995)
    Springer
    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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    Knowledge discovery from structural data (1995)
    Springer
    Journal of intelligent information systems 5 (1995), S. 229-248 
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    ISSN: 1573-7675
    Keywords: machine discovery ; data mining ; data compression ; inexact graph match ; scene analysis ; chemical analysis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Discovering repetitive substructure in a structural database improves the ability to interpret and compress the data. This paper describes the Subdue system that uses domain-independent and domain-dependent heuristics to find interesting and repetitive structures in structural data. This substructure discovery technique can be used to discover fuzzy concepts, compress the data description, and formulate hierarchical substructure definitions. Examples from the domains of scene analysis, chemical compound analysis, computer-aided design, and program analysis demonstrate the benefits of the discovery technique.
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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)
    Springer
    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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    A New ADI Scheme for Solving Three-Dimensional Parabolic Differential Equations (1997)
    Springer
    Journal of scientific computing 12 (1997), S. 361-369 
    Details
    ISSN: 1573-7691
    Keywords: Alternating-direction implicit ; difference scheme ; stability ; convergence
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A new alternating-direction implicit (ADI) scheme for solving three-dimensional parabolic differential equations has been developed based on the idea of regularized difference scheme. It is unconditionally stable and second-order accurate. Further, it overcomes the drawback of the Douglas scheme and is to be very well to simulate fast transient phenomena and to efficiently capture steady state solutions of parabolic differential equations. Numerical example is illustrated.
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    URL: http://dx.doi.org/10.1023/A:1025620828055
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    Stability of traffic patterns in broadband networks (1995)
    Springer
    Journal of network and systems management 3 (1995), S. 371-380 
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    ISSN: 1573-7705
    Keywords: Telephone traffic ; network management ; control theory ; dynamic flows ; stability ; routing algorithms ; broadband networks ; simulation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The control of telephony traffic is the task of network management and routing algorithms. In this paper, a study of two trunk groups carrying telephony traffic is used to show that instabilities can arise if there is a delay in getting feedback information for a network controller. The network controller seeks to balance the traffic in the two trunk groups, which may represent two paths from a source to a destination. An analysis shows how factors such as holding time, controller gain and feedback delay influence stability. Simulation of a two service case is also carried out to show that the same instabilities can arise.
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    URL: http://dx.doi.org/10.1007/BF02139530
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    Convergence of Delayed Dynamical Systems (1999)
    Springer
    Neural processing letters 10 (1999), S. 267-271 
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    ISSN: 1573-773X
    Keywords: recurrent neural networks ; stability
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In this paper, we point out that the conditions given in [1] are sufficient but unnecessary for the global asymptotically stable equilibrium of a class of delay differential equations. Instead, we prove that under weaker conditions, it is still global asymptotically stable.
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    URL: http://dx.doi.org/10.1023/A:1018753013035
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    Medical Data Mining on the Internet: Research on a Cancer Information System (1999)
    Springer
    Artificial intelligence review 13 (1999), S. 437-466 
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    ISSN: 1573-7462
    Keywords: CancerLit ; concept spaces ; data mining ; Hopfield net ; information retrieval ; Kohonen net ; medical knowledge ; neural networks
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper discusses several data mining algorithms and techniques thatwe have developed at the University of Arizona Artificial Intelligence Lab.We have implemented these algorithms and techniques into severalprototypes, one of which focuses on medical information developed incooperation with the National Cancer Institute (NCI) and the University ofIllinois at Urbana-Champaign. We propose an architecture for medicalknowledge information systems that will permit data mining across severalmedical information sources and discuss a suite of data mining tools that weare developing to assist NCI in improving public access to and use of theirexisting vast cancer information collections.
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    The Connectionist Inductive Learning and Logic Programming System (1999)
    Springer
    Applied intelligence 11 (1999), S. 59-77 
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    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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    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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    Computer diagnosis of goiters. V. The input (1977)
    Springer
    International journal of parallel programming 6 (1977), S. 237-261 
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    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.
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    Feature Subset Selection within a Simulated Annealing Data Mining Algorithm (1997)
    Springer
    Journal of intelligent information systems 9 (1997), S. 57-81 
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    ISSN: 1573-7675
    Keywords: Feature subset selection ; data mining ; simulated annealing
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract An overview of the principle feature subset selection methods isgiven. We investigate a number of measures of feature subset quality, usinglarge commercial databases. We develop an entropic measure, based upon theinformation gain approach used within ID3 and C4.5 to build trees, which isshown to give the best performance over our databases. This measure is usedwithin a simple feature subset selection algorithm and the technique is usedto generate subsets of high quality features from the databases. A simulatedannealing based data mining technique is presented and applied to thedatabases. The performance using all features is compared to that achievedusing the subset selected by our algorithm. We show that a substantialreduction in the number of features may be achieved together with animprovement in the performance of our data mining system. We also present amodification of the data mining algorithm, which allows it to simultaneouslysearch for promising feature subsets and high quality rules. The effect ofvarying the generality level of the desired pattern is alsoinvestigated.
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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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    Discovering Patterns from Large and Dynamic Sequential Data (1997)
    Springer
    Journal of intelligent information systems 9 (1997), S. 33-56 
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    ISSN: 1573-7675
    Keywords: combinatorial pattern matching ; data mining ; sequential pattern ; suffix tree ; update
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Most daily and scientific data are sequential in nature. Discoveringimportant patterns from such data can benefit the user and scientist bypredicting coming activities, interpreting recurring phenomena, extractingoutstanding similarities and differences for close attention, compressingdata, and detecting intrusion. We consider the following incrementaldiscovery problem for large and dynamic sequential data. Suppose thatpatterns were previously discovered and materialized. An update is made tothe sequential database. An incremental discovery will take advantage ofdiscovered patterns and compute only the change by accessing the affectedpart of the database and data structures. In addition to patterns, thestatistics and position information of patterns need to be updated to allowfurther analysis and processing on patterns. We present an efficientalgorithm for the incremental discovery problem. The algorithm is applied tosequential data that honors several sequential patterns modeling weatherchanges in Singapore. The algorithm finds what it is supposed to find.Experiments show that for small updates and large databases, the incrementaldiscovery algorithm runs in time independent of the data size.
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    Splitting Methods for Three-Dimensional Transport Models with Interaction Terms (1997)
    Springer
    Journal of scientific computing 12 (1997), S. 215-231 
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    ISSN: 1573-7691
    Keywords: Transport models ; shallow water ; splitting methods ; stability
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We investigate the use of splitting methods for the numerical integration of three-dimensional transport-chemistry models. In particular, we investigate various possibilities for the time discretization that can take advantage of the parallelization and vectorization facilities offered by multi-processor vector computers. To suppress wiggles in the numerical solution, we use third-order, upwind-biased discretization of the advection terms, resulting in a five-point coupling in each direction. As an alternative to the usual splitting functions, such as co-ordinate splitting or operator splitting, we consider a splitting function that is based on a three-coloured hopscotch-type splitting in the horizontal direction, whereas full coupling is retained in the vertical direction. Advantages of this splitting function are the easy application of domain decomposition techniques and unconditional stability in the vertical, which is an important property for transport in shallow water. The splitting method is obtained by combining the hopscotch-type splitting function with various second-order splitting formulae from the literature. Although some of the resulting methods are highly accurate, their stability behaviour (due to horizontal advection) is quite poor. Therefore we also discuss several new splitting formulae with the aim to improve the stability characteristics. It turns out that this is possible indeed, but the price to pay is a reduction of the accuracy. Therefore, such methods are to be preferred if accuracy is less crucial than stability; such a situation is frequently encountered in solving transport problems. As part of the project TRUST (Transport and Reactions Unified by Splitting Techniques), preliminary versions of the schemes are implemented on the Cray C98 4256 computer and are available for benchmarking.
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    A Generalized Peaceman–Rachford ADI Scheme for Solving Two-Dimensional Parabolic Differential Equations (1997)
    Springer
    Journal of scientific computing 12 (1997), S. 353-360 
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    ISSN: 1573-7691
    Keywords: Alternating-direction implicit ; difference scheme ; stability ; convergence
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A generalized Peaceman–Rachford alternating-direction implicit (ADI) scheme for solving two-dimensional parabolic differential equations has been developed based on the idea of regularized difference scheme. It is to be very well to simulate fast transient phenomena and to efficiently capture steady state solutions of parabolic differential equations. Numerical example is illustrated.
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    Computationally Efficient Approximation of a Probabilistic Model for Document Representation in the WEBSOM Full-Text Analysis Method (1997)
    Springer
    Neural processing letters 5 (1997), S. 69-81 
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    ISSN: 1573-773X
    Keywords: data mining ; feature extraction ; information retrieval ; Self-Organizing Map (SOM) ; text analysis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract WEBSOM is a recently developed neural method for exploring full-text document collections, for information retrieval, and for information filtering. In WEBSOM the full-text documents are encoded as vectors in a document space somewhat like in earlier information retrieval methods, but in WEBSOM the document space is formed in an unsupervised manner using the Self-Organizing Map algorithm. In this article the document representations the WEBSOM creates are shown to be computationally efficient approximations of the results of a certain probabilistic model. The probabilistic model incorporates information about the similarity of use of different words to take into account their semantic relations.
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    URL: http://dx.doi.org/10.1023/A:1009618125967
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    Hierarchical integration of sensor data and contextual information for automatic target recognition (1995)
    Springer
    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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    URL: http://dx.doi.org/10.1007/BF00872226
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    Comparative Performance of Rule Quality Measures in an Induction System (1997)
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    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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    Inductive specification recovery: Understanding software by learning from example behaviors (1995)
    Springer
    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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    URL: http://dx.doi.org/10.1007/BF00871824
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    Borders: An Efficient Algorithm for Association Generation in Dynamic Databases (1999)
    Springer
    Journal of intelligent information systems 12 (1999), S. 61-73 
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    ISSN: 1573-7675
    Keywords: association rules ; knowledge discovery ; data mining
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We consider the problem of finding association rules in a database with binary attributes. Most algorithms for finding such rules assume that all the data is available at the start of the data mining session. In practice, the data in the database may change over time, with records being added and deleted. At any given time, the rules for the current set of data are of interest. The naive, and highly inefficient, solution would be to rerun the association generation algorithm from scratch following the arrival of each new batch of data. This paper describes the Borders algorithm, which provides an efficient method for generating associations incrementally, from dynamically changing databases. Experimental results show an improved performance of the new algorithm when compared with previous solutions to the problem.
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    Data Mining in Large Databases Using Domain Generalization Graphs (1999)
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    Journal of intelligent information systems 13 (1999), S. 195-234 
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    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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    On the Accuracy of Meta-learning for Scalable Data Mining (1997)
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    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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    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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    A decision method for nonmonotonic reasoning based on autoepistemic reasoning (1995)
    Springer
    Journal of automated reasoning 14 (1995), S. 3-42 
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    ISSN: 1573-0670
    Keywords: automated theorem proving ; autoepistemic logic ; default logic
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A novel decision method for autoepistemic reasoning is developed and proved correct. The method is applicable in a general setting, i.e., for an autoepistemic logic based on a given classical logic. It provides a decision procedure for a tightly grounded from of autoepistemic reasoning based on L-hierarchic expansions as well as for autoepistemic reasoning based on Moorestyle expansions and N-expansions. Prominent formalizations of nonmonotonic reasoning, such as default logic and circumscription, can be embedded into autoepistemic logic based on L-hierarchic expansions using simple local translations. Hence, the method can serve as a unified reasoning tool for a wide range of forms of nonmonotonic reasoning. The method is conceptually simple, and the inherent sources of complexity and targets for optimization are clearly identifiable. As an example of exploiting optimization possibilities, a new decision method for Reiter's default logic is developed where ideas from autoepistemic reasoning are used to prune the search space for applicable default rules when constructing extensions of a default theory.
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    Decidability by Resolution for Propositional Modal Logics (1999)
    Springer
    Journal of automated reasoning 22 (1999), S. 379-396 
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    ISSN: 1573-0670
    Keywords: automated theorem proving ; resolution decision procedures ; propositional modal logic
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The paper shows that satisfiability in a range of popular propositional modal systems can be decided by ordinary resolution procedures. This follows from a general result that resolution combined with condensing, and possibly some additional form of normalization, is a decision procedure for the satisfiability problem in certain so-called path logics. Path logics arise from normal propositional modal logics by the optimized functional translation method. The decision result provides an alternative method of proving decidability for modal logics, as well as closely related systems of artificial intelligence. This alone is not interesting. A more far-reaching consequence of the result has practical value, namely, many standard first-order theorem provers that are based on resolution are suitable for facilitating modal reasoning.
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    Mechanical verification of strategies (1995)
    Springer
    Journal of automated reasoning 15 (1995), S. 69-93 
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    ISSN: 1573-0670
    Keywords: automated theorem proving ; Boyer-Moore theorem prover ; artificial intelligence ; commonsense reasoning ; planning ; 68T15
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper presents a method of representing planning domains in the Boyer-Moore logic so that we can prove mechanically whether a strategy solves a problem. Current approaches require explicit frame axioms and state constraints to be included as part of a domain specification and use a programming language for expressing strategies. These make it difficult to specify domains and verify plans efficiently. Our method avoids explicit frame axioms, since domains are specified by programming an interpreter for sequences of actions in the Boyer-Moore logic. Strategies are represented as ‘planners’, Lisp programs that take an initial state and other arguments as input and return a sequence of actions that, when executed in the given initial state, will bring about a goal state. Mechanical verification of a strategy is accomplished by proving that the corresponding planner solves all instances of the given problem. We illustrate our approach by verifying strategies in some variations of the blocks world.
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    URL: http://dx.doi.org/10.1007/BF00881831
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    The Design of the CADE-13 ATP System Competition (1997)
    Springer
    Journal of automated reasoning 18 (1997), S. 139-162 
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    ISSN: 1573-0670
    Keywords: automated theorem proving ; competition ; design
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Running a competition for automated theorem proving (ATP) systems is a difficult and arguable venture. However, the potential benefits of such an event by far outweigh the controversial aspects. The motivations for running the CADE-13 ATP System Competition were to contribute to the evaluation of ATP systems, to stimulate ATP research and system development, and to expose ATP systems to researchers both within and outside the ATP community. This article identifies and discusses the issues that determine the nature of such a competition. Choices and motivated decisions for the CADE-13 competition, with respect to the issues, are given.
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    LINUS - A Link Instantion Prover with Unit Support (1997)
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    Journal of automated reasoning 18 (1997), S. 205-210 
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    ISSN: 1573-0670
    Keywords: automated theorem proving ; competition ; LINUS ; first-order logic ; hyperlinking ; mate saturation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract LINUS is a theorem prover for clause logic based on hyperlinking. The main new feature of the system is the emphasis on the preference for unit clauses, which are generated as a by-product of the hyperlinking method. The mechanism of unit support also motivates a slightly different interpretation of hyperlinking, namely, as mate saturation. Mate saturation can be viewed as a generalization of unit-resulting resolution that is compatible with any set-of-support strategy. We give a survey on the theoretical background and the architecture of the system, and decribe its performance.
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    Satchmo - The Compiling and Functional Variants (1997)
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    Journal of automated reasoning 18 (1997), S. 227-236 
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    ISSN: 1573-0670
    Keywords: automated theorem proving ; competition ; Satchmo ; compilation ; incremental evaluation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Compiling Satchmo and Functional Satchmo are two variants of the model generator Satchmo, incorporating enhancements in different directions. Compiling Satchmo is based on the observation that Satchmo (like any model generator or theorem prover) can be seen as an interpreter for a program given as a logical theory, and that this interpretation layer can be avoided by compilation of the theory into a directly executable program. Functional Satchmo is an implementation of Satchmo's calculus in a purely functional language supporting lazy evaluation.
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    The Results - of the CADE-13 ATP System Competition (1997)
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    Journal of automated reasoning 18 (1997), S. 271-286 
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    ISSN: 1573-0670
    Keywords: automated theorem proving ; competition ; results
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The CADE-13 ATP System Competition tested 18 ATP systems on 50 theorems, in five competition categories, with a time limit of 300 seconds imposed on each system run. This article records the results of the competition. Some analysis of these results is given, and interesting points are highlighted.
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    Barcelona (1997)
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    Journal of automated reasoning 18 (1997), S. 171-176 
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    ISSN: 1573-0670
    Keywords: automated theorem proving ; competition ; Barcelona ; data structures and algorithms ; implementation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Here we describe the equational theorem prover Barcelona, in its version that participated in the CADE-13 ATP System Competition. The system was built on top of our toolkit of data structures and algorithms for automated deduction in first-order logic with equality and was devised mainly to test the performance of this toolkit.
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    DISCOUNT - A Distributed and Learning Equational Prover (1997)
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    Journal of automated reasoning 18 (1997), S. 189-198 
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    ISSN: 1573-0670
    Keywords: automated theorem proving ; competition ; DISCOUNT ; distributed theorem proving ; reactive planning ; learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The DISCOUNT system is a distributed equational theorem prover based on the teamwork method for knowledge-based distribution. It uses an extended version of unfailing Knuth–Bendix completion that is able to deal with arbitrarily quantified goals. DISCOUNT features many different control strategies that cooperate using the teamwork approach. Competition between multiple strategies, combined with reactive planning, results in an adaptation of the whole system to given problems, and thus in a very high degree of independence from user interaction. Teamwork also provides a suitable framework for the use of control strategies based on learning from previous proof experiences. One of these strategies forms the core of the expert global_learn, which is capable of learning from successful proofs of several problems. This expert, running sequentially, was one of the entrants in the competition (DISCOUNT/GL), while a distributed DISCOUNT system running on two workstations was another en trant.
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    SPASS - Version 0.49 (1997)
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    Journal of automated reasoning 18 (1997), S. 247-252 
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    ISSN: 1573-0670
    Keywords: automated theorem proving ; competition ; SPASS ; sorts ; superposition
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This article describes SPASS, Version 0.49, as it was entered in the system competition at CADE-13. SPASS is an automated theorem prover for full first-order logic with equality. It is based on the superposition calculus originally developed by Bachmair and Ganzinger, extended by the sort techniques due to Weidenbach and an inference rule for case analysis.
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    Violet (1997)
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    Journal of automated reasoning 18 (1997), S. 259-264 
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    ISSN: 1573-0670
    Keywords: automated theorem proving ; competition ; Violet ; resolution ; locking ; term rewriting ; Knuth–Bendix completion
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Violet is an easy-to-use theorem prover based on locking resolution, with integrated equality extensions that use term rewriting and Knuth–Bendix completion. Violet participated in the CADE-13 ATP System Competition.
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    The CADE-15 ATP System Competition (1999)
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    Journal of automated reasoning 23 (1999), S. 1-23 
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    ISSN: 1573-0670
    Keywords: competition ; automated theorem proving
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The results of the CADE-15 ATP System Competition (CASC-15) are presented.
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    SETHEO and E-SETHEO - The CADE-13 Systems (1997)
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    Journal of automated reasoning 18 (1997), S. 237-246 
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    ISSN: 1573-0670
    Keywords: automated theorem proving ; competition ; SETHEO ; E-SETHEO ; first-order logic ; model elimination ; equality
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The model elimination theorem prover SETHEO (version V3.3) and its equational extension E-SETHEO are presented. SETHEO employs sophisticated mechanisms of subgoal selection, elaborate iterative deepening techniques, and local failure caching methods. Its equational counterpart E-SETHEO transforms formulae containing equality (using a variant of Brand's modification method) and processes the output with the standard SETHEO system. This article gives an overview of the theoretical background, the system architecture, and the performance of both systems.
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    WALDMEISTER - High-Performance Equational Deduction (1997)
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    Journal of automated reasoning 18 (1997), S. 265-270 
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    ISSN: 1573-0670
    Keywords: automated theorem proving ; competition ; Waldmeister ; unfailing Knuth–Bendix completion
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Waldmeister is a high-performance theorem prover for unit equational first-order logic. In the making of Waldmeister, we have applied an engineering approach, identifying the critical points with respect to efficiency in time and space. Our logical three-level system model consists of the basic operations on the lowest level, where we put great stress on efficient data structures and algorithms. For the middle level, where the inference steps are aggregated into an inference machine, flexible adjustment has proven essential during experimental evaluation. The top level holds control strategy and reduction ordering. Although at this level only standard strategies are employed, really large proof tasks have been managed in reasonable time.
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    Unification Algorithms for Eliminating and Introducing Quantifiers in Natural Deduction Automated Theorem Proving (1997)
    Springer
    Journal of automated reasoning 18 (1997), S. 105-134 
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    ISSN: 1573-0670
    Keywords: automated theorem proving ; Gentzen system ; natural deduction ; unification algorithm
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A natural deduction system was adapted from Gentzen system. It enables valid wffs to be deduced in a very ‘natural’ way. One need not transform a formula into other normal forms. Robinson’s unification algorithm is used to handle clausal formulas. Algorithms for eliminating and introducing quantifiers without Skolemization are presented, and unification theorems for them are proved. A natural deduction automated theorem prover based on the algorithms was implemented. The rules for quantifiers are controlled by the algorithms. The Andrews challenge and the halting problem were proved by the system.
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    The Procedures of the CADE-13 ATP System Competition (1997)
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    Journal of automated reasoning 18 (1997), S. 163-169 
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    ISSN: 1573-0670
    Keywords: automated theorem proving ; competition ; procedures
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This article describes the practical procedures that were used to run the CADE-13 ATP System Competition. The article describes the hardware and software environments, the system installation, the soundness testing performed, the preparation of problems for the competition, the choice of the number of problems and the time limit, and the execution of the systems.
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    Gandalf (1997)
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    Journal of automated reasoning 18 (1997), S. 199-204 
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    ISSN: 1573-0670
    Keywords: automated theorem proving ; competition ; Gandalf ; resolution ; subsumption
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We give a brief overview of the first-order classical logic component in the Gandalf family of resolution-based automated theorem provers for classical and intuitionistic logics. The main strength of the described version is a sophisticated algorithm for nonunit subsumption.
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    RRTP - A Replacement Rule Theorem Prover (1997)
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    Journal of automated reasoning 18 (1997), S. 221-226 
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    ISSN: 1573-0670
    Keywords: automated theorem proving ; competition ; RRTP ; replacement ; instance based theorem prover ; propositional calculus decision procedure
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The Replacement Rule Theorem Prover (RRTP) is an instance-based, refutational, first-order clausal theorem prover. The prover is motivated by the idea of selectively replacing predicates by their definitions, and operates by selecting relevant instances of the input clauses. The relevant instances are grounded, if necessary, and tested for unsatisfiability by using a fast propositional calculus decision procedure.
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    SPTHEO - A Parallel Theorem Prover (1997)
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    Journal of automated reasoning 18 (1997), S. 253-258 
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    ISSN: 1573-0670
    Keywords: automated theorem proving ; competition ; SPTHEO ; parallel search ; static partitioning with slackness
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract SPTHEO v3.3 is a parallelization of the sequential first-order theorem prover SETHEO v3.3. The parallelization is based on the SPS-model (Static Partitioning with Slackness) for parallel search, an approach that minimizes the processor-to-processor communication. This model allows efficient computations on hardware with weak communication performance, such as workstation networks. SPTHEO offers the utilization of both OR- and independent-AND parallelism. In this article, a detailed description and evaluation of the OR-parallel part used in the CADE-13 ATP System Competition are given.
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    Conclusions about the CADE-13 ATP System Competition (1997)
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    Journal of automated reasoning 18 (1997), S. 287-296 
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    ISSN: 1573-0670
    Keywords: automated theorem proving ; competition ; conclusions
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The CADE-13 ATP System Competition was the first large-scale controlled competition for first-order ATP systems. Many people have commented on various aspects of the competition, including some suggestions for future improvement. These comments, and some discussion of them, are contained in this article. An overview of the major issues that will affect future competitions is given.
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    Automated production of traditional proofs in solid geometry (1995)
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    Journal of automated reasoning 14 (1995), S. 257-291 
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    ISSN: 1573-0670
    Keywords: automated theorem proving ; Euclidean traditional proofs ; volume method ; constructive geometry statements
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper presents a method of producing readable proofs for theorems in solid geometry. The method is for a class of constructive geometry statements about straight lines, planes, circles, and spheres. The key idea of the method is to eliminate points from the conclusion of a geometric statement using several (fixed) high-level basic propositions about the signed volumes of tetrahedrons and Pythagorean differences of triangles. We have implemented the algorithm, and more than 80 examples from solid geometry have been used to test the program. Our program is efficient and the proofs produced by it are generally short and readable.
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    CLIN-S - A Semantically Guided First-Order Theorem Prover (1997)
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    Journal of automated reasoning 18 (1997), S. 183-188 
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    ISSN: 1573-0670
    Keywords: automated theorem proving ; competition ; CLIN-S ; semantics ; hyper-linking ; resolution
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract CLIN-S is an instance-based, clause-form first-order theorem prover. CLIN-S employs three inference procedures: semantic hyper-linking, which uses semantics to guide the proof search and performs well on non-Horn parts of the proofs involving small literals, rough resolution, which removes large literals in the proofs, and UR resolution, which proves the Horn parts of the proofs. A semantic structure for the input clauses is given as input. During the search for the proof, ground instances of the input clauses are generated and new semantic structures are built based on the input semantics and a model of the ground clause set. A proof is found if the ground clause set is unsatisfiable. In this article, we describe the system architecture and major inference rules used in CLIN-S.
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    CLIN-E - Smallest Instance First Hyper-Linking (1997)
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    Journal of automated reasoning 18 (1997), S. 177-182 
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    ISSN: 1573-0670
    Keywords: automated theorem proving ; competition ; CLIN-E ; hyper-linking ; smallest instance first hyper-linking
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Hyper-linking is an instance-based automated theorem proving strategy that uses unification to generate instances of the input clauses. Lee implemented hyper-linking in the automated theorem prover CLIN, which uses a breadth-first strategy for generating instances of clauses via the hyper-link operation. In attempting to add equality support to CLIN, a number of inefficiencies with Lee's breadth-first strategy for generating instances were encountered. An alternative depth-first strategy, referred to as smallest-instance-first hyper-linking, for generating instances via the hyper-link operation was developed to address these inefficiencies. Smallest-instance-first hyper-linking is implemented in the automated theorem prover CLIN-E.
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    Otter - The CADE-13 Competition Incarnations (1997)
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    Journal of automated reasoning 18 (1997), S. 211-220 
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    Keywords: automated theorem proving ; competition ; Otter ; automated reasoning ; equational deduction ; paramodulation ; resolution
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This article discusses the two incarnations of Otter entered in the CADE-13 Automated Theorem Proving System Competition. Also presented are some historical background, a summary of applications that have led to new results in mathematics and logic, and a general discussion of Otter.
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    Multicategory Classification by Support Vector Machines (1999)
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    Computational optimization and applications 12 (1999), S. 53-79 
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    ISSN: 1573-2894
    Keywords: support vector machines ; linear programming ; classification ; data mining ; machine learning.
    Source: Springer Online Journal Archives 1860-2000
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    Notes: Abstract We examine the problem of how to discriminate between objects of three or more classes. Specifically, we investigate how two-class discrimination methods can be extended to the multiclass case. We show how the linear programming (LP) approaches based on the work of Mangasarian and quadratic programming (QP) approaches based on Vapnik's Support Vector Machine (SVM) can be combined to yield two new approaches to the multiclass problem. In LP multiclass discrimination, a single linear program is used to construct a piecewise-linear classification function. In our proposed multiclass SVM method, a single quadratic program is used to construct a piecewise-nonlinear classification function. Each piece of this function can take the form of a polynomial, a radial basis function, or even a neural network. For the k 〉 2-class problems, the SVM method as originally proposed required the construction of a two-class SVM to separate each class from the remaining classes. Similarily, k two-class linear programs can be used for the multiclass problem. We performed an empirical study of the original LP method, the proposed k LP method, the proposed single QP method and the original k QP methods. We discuss the advantages and disadvantages of each approach.
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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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    Control-Sensitive Feature Selection for Lazy Learners (1997)
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    Artificial intelligence review 11 (1997), S. 227-253 
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    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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    Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals (1997)
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    Data mining and knowledge discovery 1 (1997), S. 29-53 
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    ISSN: 1573-756X
    Keywords: data cube ; data mining ; aggregation ; summarization ; database ; analysis ; query
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Data analysis applications typically aggregate data across manydimensions looking for anomalies or unusual patterns. The SQL aggregatefunctions and the GROUP BY operator produce zero-dimensional orone-dimensional aggregates. Applications need the N-dimensionalgeneralization of these operators. This paper defines that operator, calledthe data cube or simply cube. The cube operator generalizes the histogram,cross-tabulation, roll-up,drill-down, and sub-total constructs found in most report writers.The novelty is that cubes are relations. Consequently, the cubeoperator can be imbedded in more complex non-procedural dataanalysis programs. The cube operator treats each of the Naggregation attributes as a dimension of N-space. The aggregate ofa particular set of attribute values is a point in this space. Theset of points forms an N-dimensional cube. Super-aggregates arecomputed by aggregating the N-cube to lower dimensional spaces.This paper (1) explains the cube and roll-up operators, (2) showshow they fit in SQL, (3) explains how users can define new aggregatefunctions for cubes, and (4) discusses efficient techniques tocompute the cube. Many of these features are being added to the SQLStandard.
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    Advanced Scout: Data Mining and Knowledge Discovery in NBA Data (1997)
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    Data mining and knowledge discovery 1 (1997), S. 121-125 
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    ISSN: 1573-756X
    Keywords: data mining ; knowledge discovery ; attribute focusing ; basketball ; NBA
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Advanced Scout is a PC-based data mining application used by National Basketball Association (NBA)coaching staffs to discover interesting patterns in basketball game data. We describe Advanced Scout software from the perspective of data mining and knowledge discovery. This paper highlights the pre-processing of raw data that the program performs, describes the data mining aspects of the software and how the interpretation of patterns supports the processof knowledge discovery. The underlying technique of attribute focusing asthe basis of the algorithm is also described. The process of pattern interpretation is facilitated by allowing the user to relate patterns to video tape.
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    A Simple Constraint-Based Algorithm for Efficiently Mining Observational Databases for Causal Relationships (1997)
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    Data mining and knowledge discovery 1 (1997), S. 203-224 
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    ISSN: 1573-756X
    Keywords: causal discovery ; data mining ; observational data
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper presents a simple, efficient computer-based method for discovering causal relationships from databases that contain observational data. Observational data is passively observed, as contrasted with experimental data. Most of the databases available for data mining are observational. There is great potential for mining such databases to discover causal relationships. We illustrate how observational data can constrain the causal relationships among measured variables, sometimes to the point that we can conclude that one variable is causing another variable. The presentation here is based on a constraint-based approach to causal discovery. A primary purpose of this paper is to present the constraint-based causal discovery method in the simplest possible fashion in order to (1) readily convey the basic ideas that underlie more complex constraint-based causal discovery techniques, and (2) permit interested readers to rapidly program and apply the method to their own databases, as a start toward using more elaborate causal discovery algorithms.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1009787925236
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  • 96
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    Electronic Resource
    Partitioning Nominal Attributes in Decision Trees (1999)
    Springer
    Data mining and knowledge discovery 3 (1999), S. 197-217 
    Details
    ISSN: 1573-756X
    Keywords: binary decision tree ; classification ; data mining ; entropy ; Gini index ; impurity ; optimal splitting
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract To find the optimal branching of a nominal attribute at a node in an L-ary decision tree, one is often forced to search over all possible L-ary partitions for the one that yields the minimum impurity measure. For binary trees (L = 2) when there are just two classes a short-cut search is possible that is linear in n, the number of distinct values of the attribute. For the general case in which the number of classes, k, may be greater than two, Burshtein et al. have shown that the optimal partition satisfies a condition that involves the existence of 2 L hyperplanes in the class probability space. We derive a property of the optimal partition for concave impurity measures (including in particular the Gini and entropy impurity measures) in terms of the existence ofL vectors in the dual of the class probability space, which implies the earlier condition. Unfortunately, these insights still do not offer a practical search method when n and k are large, even for binary trees. We therefore present a new heuristic search algorithm to find a good partition. It is based on ordering the attribute's values according to their principal component scores in the class probability space, and is linear in n. We demonstrate the effectiveness of the new method through Monte Carlo simulation experiments and compare its performance against other heuristic methods.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1009869804967
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  • 97
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    Electronic Resource
    CHAMP: A Prototype for Automated Cellular Churn Prediction (1999)
    Springer
    Data mining and knowledge discovery 3 (1999), S. 219-225 
    Details
    ISSN: 1573-756X
    Keywords: data mining ; knowledge discovery ; churn prediction application ; predictive modeling
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We describe CHAMP (CHurn Analysis, Modeling, and Prediction), an automated system for modeling cellular customer behavior on a large scale. Using historical data from GTE's data warehouse for cellular phone customers, every month CHAMP identifies churn factors for several geographic regions and updates models to generate churn scores predicting who is likely to churn within the near future. CHAMP is capable of developing customized monthly models and churn scores for over one hundred GTE cellular phone markets totaling over 5 million customers.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1009873905876
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  • 98
    Electronic Resource
    Electronic Resource
    Parallel Formulations of Decision-Tree Classification Algorithms (1999)
    Springer
    Data mining and knowledge discovery 3 (1999), S. 237-261 
    Details
    ISSN: 1573-756X
    Keywords: data mining ; parallel processing ; classification ; scalability ; decision trees
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Classification decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud detection, etc. Highly parallel algorithms for constructing classification decision trees are desirable for dealing with large data sets in reasonable amount of time. Algorithms for building classification decision trees have a natural concurrency, but are difficult to parallelize due to the inherent dynamic nature of the computation. In this paper, we present parallel formulations of classification decision tree learning algorithm based on induction. We describe two basic parallel formulations. One is based on Synchronous Tree Construction Approach and the other is based on Partitioned Tree Construction Approach. We discuss the advantages and disadvantages of using these methods and propose a hybrid method that employs the good features of these methods. We also provide the analysis of the cost of computation and communication of the proposed hybrid method. Moreover, experimental results on an IBM SP-2 demonstrate excellent speedups and scalability.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1009832825273
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  • 99
    Electronic Resource
    Electronic Resource
    Effect of Data Distribution in Parallel Mining of Associations (1999)
    Springer
    Data mining and knowledge discovery 3 (1999), S. 291-314 
    Details
    ISSN: 1573-756X
    Keywords: association rules ; data mining ; data skewness ; workload balance ; parallel mining ; parallel computing
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Association rule mining is an important new problem in data mining. It has crucial applications in decision support and marketing strategy. We proposed an efficient parallel algorithm for mining association rules on a distributed share-nothing parallel system. Its efficiency is attributed to the incorporation of two powerful candidate set pruning techniques. The two techniques, distributed and global prunings, are sensitive to two data distribution characteristics: data skewness and workload balance. The prunings are very effective when both the skewness and balance are high. We have implemented FPM on an IBM SP2 parallel system. The performance studies show that FPM outperforms CD consistently, which is a parallel version of the representative Apriori algorithm (Agrawal and Srikant, 1994). Also, the results have validated our observation on the effectiveness of the two pruning techniques with respect to the data distribution characteristics. Furthermore, it shows that FPM has nice scalability and parallelism, which can be tuned for different business applications.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1009836926181
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  • 100
    Electronic Resource
    Electronic Resource
    The Carnot Heterogeneous Database Project: Implemented Applications (1997)
    Springer
    Distributed and parallel databases 5 (1997), S. 207-225 
    Details
    ISSN: 1573-7578
    Keywords: enterprise integration ; workflow management ; agents interoperation ; heterogeneous databases ; scientific decision support ; data mining
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The Carnot project was an ambitious research project inheterogeneous databases. It integrated a variety of techniques toaddress a wide range of problems in achieving interoperation inheterogeneous environments. Here we describe some of the majorimplemented applications of this project. These applications concern(a) accessing a legacy scientific database, (b) automating a workflowinvolving legacy systems, (c) cleaning data, and (d) retrievingsemantically appropriate information from structured databases inresponse to text queries. These applications support scientificdecision support, business process management, data integrityenhancement, and analytical decision support, respectively. Theydemonstrate Carnot‘s capabilities for (a) heterogeneous queryprocessing, (b) relaxed transaction and workflow management, (c)knowledge discovery, and (d) heterogeneous resource modelintegration.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008645509474
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