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  • 1
    Monograph available for loan
    Monograph available for loan
    Berlin : Akademie-Verl.
    Associated volumes
    Call number: MOP B 17629
    In: Informatik, Kybernetik, Rechentechnik
    Type of Medium: Monograph available for loan
    Pages: XI, 294 S. : graph. Darst.
    Series Statement: Informatik, Kybernetik, Rechentechnik 2.
    Location: MOP - must be ordered
    Branch Library: GFZ Library
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Applied intelligence 11 (1999), S. 15-30 
    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.
    Type of Medium: Electronic Resource
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  • 3
    ISSN: 0949-2925
    Keywords: Schlüsselwörter: Künstliche Intelligenz, Maschinelles Lernen, Graphen, Induktive Logische Programmierung, Knowledge Discovery in Databases, Entscheidungsbaumverfahren, Subsumtion, LGG ; Key words: Artificial Intelligence, Machine Learning, Graphs, Inductive Logic Programming, Knowledge Discovery in Databases, Decision Trees, Subsumption, LGG ; CR Subject Classification: I.2.6, G.2.2, I.2.3
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Description / Table of Contents: Abstract. In this article, we discuss the problem of learning a classifier for relational data with two application examples from Inductive Logic Programming (ILP). We present important techniques and questions of graph based and logical learning, especially its computational complexity. Based on this discussion, the learning system Indigo is described, which relies on the efficient graph based transformation of the relational learning task into a feature based problem, which can be solved with classical feature based methods like CAL3 or ID3. Using Indigo as an example, it can be seen that graph theoretical approaches can make an important contribution to relational machine learning, that cannot be achieved in the same way with logical methods stemming from the field of ILP. Therefore, one concern of this work is the comparison of graph theoretical and logical learning methods. This is done by describing the hybrid learning system Tritop that uses graph theoretical methods together with a logical notation and ILP concepts like subsumtion of clauses. Tritop uses the so called $\alpha$ -subsumption, a restriction of the well known $\theta$ -subsumption. As an extension of the two learning systems, we also discuss the efficient construction of class prototypes using a neual network.
    Notes: Zusammenfassung. Anhand zweier Anwendungsbeispiele aus der Induktiven Logischen Programmierung (ILP) wird die Fragestellung des Erlernens von Klassifikatoren für relational strukturierte Beispiele betrachtet. Es wird auf wesentliche Techniken und Fragen des graphbasierten und logischen Lernens, insbesondere auf Fragen des Aufwands eingegangen. Darauf aufbauend wird das Verfahren Indigo zur effizienten graphentheoretischen Transformation des relationalen in ein merkmalsbasiertes Lernproblem vorgestellt, welches den Einsatz von klassischen Lernverfahren wie CAL3 oder ID3 erlaubt. Am Beispiel von Indigo wird gezeigt, daß spezifisch graphentheoretische Ansätze und Algorithmen einen wertvollen Beitrag zur Behandlung von relationalen Lernproblemen erbringen können, der mit rein logischen Methoden, wie sie derzeit in der ILP untersucht werden, nicht entsprechend erzielt werden kann. Eine Aufgabenstellung dieser Arbeit ist deshalb die Gegenüberstellung von graphbasierten und logischen Lerntechniken am Beispiel des hybriden Lernverfahrens Tritop, welches Grundideen aus der Graphentheorie in einem logischen Gewand verwirklicht. Tritop verwendet die sog. $\alpha$ -Subsumtion, eine Spezialisierung der bekannten $\theta$ -Subsumtion, zur Klassifikation von Beispielen. Neben den beiden Lernverfahren gehen wir auf die effiziente Konstruktion von Klassenprototypen mit einem konnektionistischen Verfahren ein.
    Type of Medium: Electronic Resource
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  • 4
    ISSN: 1572-9338
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract An algorithm for learning decision trees for classification and prediction is described which converts real-valued attributes into intervals using statistical considerations. The trees are automatically pruned with the help of a threshold for the estimated class probabilities in an interval. By means of this threshold the user can control the complexity of the tree, i.e. the degree of approximation of class regions in feature space. Costs can be included in the learning phase if a cost matrix is given. In this case class dependent thresholds are used. Some applications are described, especially the task of predicting the high water level in a mountain river.
    Type of Medium: Electronic Resource
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