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