Abstract
Analogy is clearly one of the key issues of Learning, since it constitutes a major tool for constructing new concepts, or new rules or new strategies. Our view of analogy is that it must take into account not only the resemblances within a set of different datas but also the differences among them. We describe how to apply this view of the analogical process to incremental similarity-based learning.
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© 1989 Springer-Verlag Berlin Heidelberg
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Vrain, C., Kodratoff, Y. (1989). The use of analogy in incremental SBL. In: Morik, K. (eds) Knowledge Representation and Organization in Machine Learning. Lecture Notes in Computer Science, vol 347. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0017225
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DOI: https://doi.org/10.1007/BFb0017225
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