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- W1569593388 abstract "In this article, PAC-learning theory is applied to model inference, which concerns the problem of inferring theories from facts in first order logic. It is argued that uniform sample PAC-learnability cannot be expected with most of the ‘interesting’ model classes. Polynomial sample learnability can only be accomplished in classes of programs having a fixed maximum number of clauses. We have proved that the class of context free programs in a fixed maximum number of clauses with a fixed maximum number of literals is learnable from a polynomial number of examples. This is also proved for a more general class of programs." @default.
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- W1569593388 date "1994-01-01" @default.
- W1569593388 modified "2023-09-26" @default.
- W1569593388 title "Sample PAC-learnability in model inference" @default.
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- W1569593388 doi "https://doi.org/10.1007/3-540-57868-4_60" @default.
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