DMCA
Learnability and the Vapnik-Chervonenkis dimension (1989)
Cached
Download Links
| Citations: | 726 - 21 self |
BibTeX
@MISC{Blumer89learnabilityand,
author = {Anselm Blumer and ANDRZEJ EHRENFEUCHT and David Haussler and Manfred K. Warmuth},
title = {Learnability and the Vapnik-Chervonenkis dimension},
year = {1989}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Valiant’s learnability model is extended to learning classes of concepts defined by regions in Euclidean space E”. The methods in this paper lead to a unified treatment of some of Valiant’s results, along with previous results on distribution-free convergence of certain pattern recognition algorithms. It is shown that the essential condition for distribution-free learnability is finiteness of the Vapnik-Chervonenkis dimension, a simple combinatorial parameter of the class of concepts to be learned. Using this parameter, the complexity and closure properties of learnable classes are analyzed, and the necessary and sufftcient conditions are provided for feasible learnability.
Keyphrases
distribution-free learnability feasible learnability distribution-free convergence valiant result valiant learnability model problem complexity sufftcient condition simple combinatorial parameter unified treatment subject descriptor vapnik-chervonenkis dimension learnable class essential condition closure property certain pattern recognition algorithm previous result paper lead euclidean space
