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On-line algorithms in machine learning

By Avrim Blum

Abstract

both concerned with problems of making decisions about the present based only on knowledge of the past. Although these areas di er in terms of their emphasis and the problems typically studied, there are a collection of results in Computational Learning Theory that t nicely into the \on-line algorithms " framework. This survey article discusses some of the results, models, and open problems from Computational Learning Theory that seem particularly interesting from the point of view of on-line algorithms. The emphasis in this article is on describing some of the simpler, more intuitive results, whose proofs can be given in their entirity. Pointers to the literature are given for more sophisticated versions of these algorithms.

Year: 1996
OAI identifier: oai:CiteSeerX.psu:10.1.1.190.3552
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