Abstract
This paper introduces a new framework for constructing learning algorithms. Our methods involve master algorithms which use learning algorithms for intersection-closed concept classes as subroutines. For ex- ample, we give a master algorithm capable of learning any concept class whose members can be expressed as nested differences (for example, c1 - (c2 - (c 3 - (c4 — c5)))) of concepts from an intersection-closed class. We show that our algorithms are optimal or nearly optimal with respect to several different criteria. These criteria include: the number of examples needed to produce a good hypothesis with high confidence, the worst case total number of mistakes made, and the expected number of mistakes made in the first t trials.
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