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Open Access Publications from the University of California

The utility of knowledge in inductive learning

  • Author(s): Pazzani, Michael
  • Kibler, Dennis
  • et al.

In this paper, we demonstrate how different forms of background knowledge can be integrated with an inductive method for generating constant-free Horn clause rules. Furthermore, we evaluate, both theoretically and empirically, the effect that these types of knowledge have on the cost of learning a rule and on the accuracy of a learned rule. Moreover, we demonstrate that a hybrid explanation-based and inductive learning method can advantageously use an approximate domain theory, even when this theory is incorrect and incomplete.

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