The Utility of Knowledge in Inductive Learning

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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. © 1992, Kluwer Academic Publishers. All rights reserved.

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Pazzani, M., & Kibler, D. (1992). The Utility of Knowledge in Inductive Learning. Machine Learning, 9(1), 57–94. https://doi.org/10.1023/A:1022628829777

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