Wendy Sarrett

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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 explarxxtionbased 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)
  • June, EDITORAWORKSHOP CHAIR, +10 authors Tom Fawcett
  • ML
  • 1989
This paper presents a new approach to combining explanation-based and empirical learning called Induction Over the Unexplained (IOU). Unlike other approaches to integrated learning, which use one method to focus the other or provide it with information, IOU uses each method to learn a different part of the final concept definition. It is therefore suited(More)
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