Accession Number : ADA330965
Title : On-Line Algorithms in Machine Learning
Corporate Author : CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE
Personal Author(s) : Blum, Avrim L.
PDF Url : ADA330965
Report Date : JUL 1997
Pagination or Media Count : 21
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 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.
Descriptors : *ALGORITHMS, *LEARNING MACHINES, *ONLINE SYSTEMS, SEMANTICS, EXPERT SYSTEMS, SYSTEMS ANALYSIS.
Subject Categories : Cybernetics
Distribution Statement : APPROVED FOR PUBLIC RELEASE