Abstract
An induction-based method for retrieving similar cases and/or easily adaptable cases is presented in a 3-steps process: first, a rule set is learned from a data set; second, a reformulation of the problem domain is derived from this ruleset; third, a surface similarity with respect to the reformulated problem appears to be a structural similarity with respect to the initial representation of the domain. This method achieves some integration between machine learning and case-based reasoning: it uses both compiled knowledge (through the similarity measure and the ruleset it is derived from) and instanciated knowledge (through the cases).
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© 1994 Springer-Verlag Berlin Heidelberg
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Sebag, M., Schoenauer, M. (1994). A rule-based similarity measure. In: Wess, S., Althoff, KD., Richter, M.M. (eds) Topics in Case-Based Reasoning. EWCBR 1993. Lecture Notes in Computer Science, vol 837. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58330-0_81
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DOI: https://doi.org/10.1007/3-540-58330-0_81
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