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SVM Model Selection with the VC Bound.
- Source :
- Computational & Information Science; 2004, p1067-1071, 5p
- Publication Year :
- 2004
-
Abstract
- Model selection plays a key role in the performance of a support vector machine (SVM). In this paper, we propose two algorithms that use the Vapnik Chervonenkis (VC) bound for SVM model selection. The algorithms employ a coarse-to-fine search strategy to obtain the best parameters in some predefined ranges for a given problem. Experimental results on several benchmark datasets show that the proposed hybrid algorithm has very comparative performance with the cross validation algorithm. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540241270
- Database :
- Supplemental Index
- Journal :
- Computational & Information Science
- Publication Type :
- Book
- Accession number :
- 32716653