DocumentCode :
1547644
Title :
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 SVMlight 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
Link To Document :
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