Interest and Predictability: Deciding What to Learn, When to Learn

Michael Lebowitz
Inductive learning, which involves largely structural comparisons of examples, and explanation-based learning, a knowledge-intensive method for analyzing examples to build generalized schemas, are two major learning techniques used in AI. In this paper, we show how a combination of the two methods - applying generalization-based techniques during the course of inductive learning - can achieve the power of explanation-based learning without some of the computational problems that arise in domains lacking detailed explanatory rules. We...
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