The Utility of Knowledge in Inductive Learning

Authors:
Michael Pazzani;Dennis Kibler
Affiliations:
Department of Information & Computer Science, University of California, Irvine, Irvine, CA 92717-3425. pazzani@ics.uci.edu;Department of Information & Computer Science, University of California, Irvine, Irvine, CA 92717-3425. kibler@ics.uci.edu
Venue:
Machine Learning
Year:
1992

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Abstract

In this paper, we demonstrate how different forms of background knowledge can be integrated with an inductive method for generating function-free Horn clause rules. Furthermore, we evaluate, both theoretically and empirically, the effect that these forms of knowledge have on the cost and accuracy of learning. Lastly, 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.