This paper describes and evaluates an approach to combining empirical and explanation-based learning called Induction Over the Unexplained (IOU). IOU is intended for learning concepts that can be partially explained by an overly-general domain theory. An eclectic evaluation of the method is presented which includes results from all three major approaches: empirical, theoretical, and psychological. Empirical results shows that IOU is effective at refining overly-general domain theories and that it learns more accurate concepts from fewer examples than a purely empirical approach. The application of theoretical results from PAC learnability theory explains why IOU requires fewer examples. IOU is also shown to be able to model psychological data demonstrating the effect of background knowledge on human learning.

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Machine Learning, Vol. 10 (1993), pp. 79-110.

@Article{mooney:mlj93, title={Induction Over the Unexplained: Using Overly-General Domain Theories to Aid Concept Learning}, author={Raymond J. Mooney}, volume={10}, journal={Machine Learning}, key={IOU}, pages={79-110}, url="http://www.cs.utexas.edu/users/ai-lab?mooney:mlj93", year={1993} }