On the convergence of the decomposition method for support vector machines

Author

Lin, Chih-Jen

Author_Institution

Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ., Taipei, Taiwan

Volume

12

Issue

6

fYear

2001

fDate

11/1/2001 12:00:00 AM

Firstpage

1288

Lastpage

1298

Abstract

The decomposition method is currently one of the major methods for solving support vector machines (SVM). Its convergence properties have not been fully understood. The general asymptotic convergence was first proposed by Chang et al. However, their working set selection does not coincide with existing implementation. A later breakthrough by Keerthi and Gilbert (2000, 2002) proved the convergence finite termination for practical cases while the size of the working set is restricted to two. In this paper, we prove the asymptotic convergence of the algorithm used by the software SVM^light and other later implementation. The size of the working set can be any even number. Extensions to other SVM formulations are also discussed

Keywords

convergence; learning automata; SVM; asymptotic convergence; convergence finite termination; decomposition method convergence; support vector machines; Computer science; Convergence; Helium; Kernel; Matrix decomposition; Software algorithms; Support vector machine classification; Support vector machines; Upper bound;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.963765

Filename

963765