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On-Line Algorithms in Machine Learning
Author(s) -
Avrim Blum
Publication year - 1998
Publication title -
lecture notes in computer science
Language(s) - English
Resource type - Book series
SCImago Journal Rank - 0.249
H-Index - 400
eISSN - 1611-3349
pISSN - 0302-9743
DOI - 10.1007/bfb0029575
Subject(s) - computer science , computational learning theory , artificial intelligence , mathematical proof , point (geometry) , algorithm , machine learning , line (geometry) , emphasis (telecommunications) , analysis of algorithms , algorithmic learning theory , theoretical computer science , unsupervised learning , mathematics , telecommunications , geometry
: 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 research. The emphasis in this article is on describing some of the simpler, more intuitive results, whose proofs can be given in their entirety. Pointers to the literature are given for more sophisticated versions of these algorithms.

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