DocumentCode
1465160
Title
The one-inclusion graph algorithm is near-optimal for the prediction model of learning
Author
Li, Yi ; Long, Philip M. ; Srinivasan, Aravind
Author_Institution
Dept. of Comput. Sci., Nat. Univ. of Singapore, Singapore
Volume
47
Issue
3
fYear
2001
fDate
3/1/2001 12:00:00 AM
Firstpage
1257
Lastpage
1261
Abstract
Haussler, Littlestone and Warmuth (1994) described a general-purpose algorithm for learning according to the prediction model, and proved an upper bound on the probability that their algorithm makes a mistake in terms of the number of examples seen and the Vapnik-Chervonenkis (VC) dimension of the concept class being learned. We show that their bound is within a factor of 1+o(1) of the best possible such bound for any algorithm
Keywords
graph theory; learning systems; optimisation; prediction theory; probability; Vapnik-Chervonenkis dimension; concept class; general-purpose algorithm; learning prediction model; near-optimal algorithm; one-inclusion graph algorithm; probability; upper bound; Computer science; Neural networks; Prediction algorithms; Predictive models; Probability distribution; Upper bound; Virtual colonoscopy;
fLanguage
English
Journal_Title
Information Theory, IEEE Transactions on
Publisher
ieee
ISSN
0018-9448
Type
jour
DOI
10.1109/18.915700
Filename
915700
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