A study on SMO-type decomposition methods for support vector machines

Author

Chih-Jen Lin

Volume

17

Issue

4

fYear

2006

fDate

7/1/2006 12:00:00 AM

Firstpage

893

Lastpage

908

Abstract

Decomposition methods are currently one of the major methods for training support vector machines. They vary mainly according to different working set selections. Existing implementations and analysis usually consider some specific selection rules. This paper studies sequential minimal optimization type decomposition methods under a general and flexible way of choosing the two-element working set. The main results include: 1) a simple asymptotic convergence proof, 2) a general explanation of the shrinking and caching techniques, and 3) the linear convergence of the methods. Extensions to some support vector machine variants are also discussed.

Keywords

convergence; learning (artificial intelligence); optimisation; support vector machines; SMO-type decomposition methods; caching technique; linear convergence; sequential minimal optimization type decomposition methods; shrinking technique; simple asymptotic convergence proof; support vector machines training; working set selections; Computer science; Convergence; Iterative methods; Kernel; Matrix decomposition; Optimization methods; Support vector machines; Symmetric matrices; Upper bound; Decomposition method; sequential minimal optimization (SMO); support vector machine (SVM);

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2006.875973

Filename

1650245