Computing with structured connectionist networks
Communications of the ACM
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Applied Intelligence
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Machine Learning
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ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
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ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
A Connectionist Approach to the Distance-Based Analysis of Relational Data
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
Incident mining using structural prototypes
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
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The paper addresses the question how learning class discriminations and learning characteristic class descriptions can be related in relational learning. We present the approach TRITOP/MATCHBOX combining the relational decision tree algorithm TRITOP with the connectionist approach MATCHBOX. TRITOP constructs efficiently a relational decision tree for the fast discrimination of classes of relational descriptions, while MatchBox is used for constructing class prototypes. Although TRITOP's decision trees perform very well in the classification task, they are difficult to understand and to explain. In order to overcome this disadvantage of decision trees in general, in a second step the decision tree is supplemented by prototypes. Prototypes are generalized graph theoretic descriptions of common substructures of those subclasses of the training set that are defined by the leaves of the decision tree. Such prototypes give a comprehensive and understandable description of the subclasses. In the prototype construction, the connectionist approach MATCHBOX is used to perform fast graph matching and graph generalisation, which are originally NP-complete tasks. The constructed prototypes can be used for classification in a decision list framework, where the decision list is constructed from the decision tree.