Average Case Analysis of Conjunctive Learning Algorithms

Abstract

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 combines empirical and explanation-based learning. We evaluate the average-case models by comparing the accuracy predicted by the models to the actual accuracy obtained by running the learning algorithms.