HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Kao, W.-C. Articles by Lin, C.-J. Search for Related Content PubMed PubMed Citation Articles by Kao, W.-C. Articles by Lin, C.-J.

Decomposition Methods for Linear Support Vector Machines

b89106{at}csie.ntu.edu.tw, Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan

Kai-Min Chung

b88061{at}csie.ntu.edu.tw, Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan

Chia-Liang Sun

b88047{at}csie.ntu.edu.tw, Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan

Chih-Jen Lin

cjlin{at}csie.ntu.edu.tw, Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan

In this letter, we show that decomposition methods with alpha seeding are extremely useful for solving a sequence of linear support vector machines (SVMs) with more data than attributes. This strategy is motivated by Keerthi and Lin (2003), who proved that for an SVM with data not linearly separable, after C is large enough, the dual solutions have the same free and bounded components. We explain why a direct use of decomposition methods for linear SVMs is sometimes very slow and then analyze why alpha seeding is much more effective for linear than nonlinear SVMs. We also conduct comparisons with other methods that are efficient for linear SVMs and demonstrate the effectiveness of alpha seeding techniques in model selection.