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We present an approach to modeling the average case behavior of learning algorithms. Our motivation is to predict the expected accuracy of learning algorithms as a function of the number of training examples. We apply this framework to a purely empirical learning algorithm, (the one-sided algorithm for pure conjunctive concepts), and to an algorithm that… (More)
The purpose of this paper is to describe a framework for integrating empirical learning with explarxxtion-based learning (EBL)[DeJong & Mooney 1986; Mitchell, Keller & Kedar-Cabelli 19861 and to present an algorithm which does this with both pure conjunctive concepts and rE-CNF concepts. Our framework involves using an empirical and an explanation-based… (More)
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