On-Line Algorithms in Machine Learning

On-Line Algorithms in Machine Learning (1996)

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by Avrim Blum

Venue:	In Proceedings of the Workshop on On-Line Algorithms, Dagstuhl
Citations:	75 - 2 self

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@INPROCEEDINGS{Blum96on-linealgorithms,
    author = {Avrim Blum},
    title = {On-Line Algorithms in Machine Learning},
    booktitle = {In Proceedings of the Workshop on On-Line Algorithms, Dagstuhl},
    year = {1996},
    pages = {306--325},
    publisher = {Springer}
}

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Abstract

. The areas of On-Line Algorithms and Machine Learning are both concerned with problems of making decisions about the present based only on knowledge of the past. Although these areas differ in terms of their emphasis and the problems typically studied, there are a collection of results in Computational Learning Theory that fit 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. 1 Introduction The areas of On-Line Algorithms and Machine Learning are both concerned with problems of making decisions from limited information. Although they differ in terms of their emphasis and the...

Keyphrases

on-line algorithm machine learning computational learning theory open problem limited information on-line algorithm framework survey article intuitive result sophisticated version