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Logical settings for concept-learning
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DOI：10.1016/S0004-3702(97)00041-6.png)
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摘要
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Three different formalizations of concept-learning in logic (as well as some variants) are analyzed and related. It is shown that learning from interpretations reduces to learning from entailment, which in rum reduces to learning from satisfiability. The implications of this result for inductive logic programming and computational learning theory are then discussed, and guidelines for choosing a problem-setting are formulated. (C) 1997 Elsevier Science B.V.
Keyword:
inductive logic programming
computational learning theory
concept-learning
logic
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