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Learning Based on Conceptual Distance
November 1988 (vol. 10 no. 6)
pp. 897-909

An approach to concept learning from examples and concept learning by observation is presented that is based on a intuitive notion of conceptual distance between examples (concepts) and combines symbolical and numerical methods. The approach is based on the observation that very different examples generalize to an expression that is very far from each of them, while identical examples generalize to themselves. Following this idea the authors propose some domain-independent and intuitively justified estimates for the conceptual distance. A hierarchical conceptual clustering algorithm that groups objects so as to maximize the cohesiveness (a reciprocal of the conceptual distance) of the clusters is presented. It is shown that conceptual clustering can improve learning from complex examples describing objects and the relation between them.

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Index Terms:
symbolic methods; artificial intelligence; knowledge acquisition; conceptual distance; concept learning; numerical methods; hierarchical conceptual clustering; cohesiveness; artificial intelligence; knowledge acquisition; learning systems
Citation:
Y. Kodratoff, G. Tecuci, "Learning Based on Conceptual Distance," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, no. 6, pp. 897-909, Nov. 1988, doi:10.1109/34.9111
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