A novel sequential minimal optimization algorithm for support vector regression

Authors:
Jun Guo;Norikazu Takahashi;Tetsuo Nishi
Affiliations:
Department of Computer Science and Communication Engineering, Kyushu Univ., Fukuoka, Japan;Department of Computer Science and Communication Engineering, Kyushu Univ., Fukuoka, Japan;Faculty of Science and Engineering, Waseda Univ., Tokyo, Japan
Venue:
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Year:
2006

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Abstract

A novel sequential minimal optimization (SMO) algorithm for support vector regression is proposed. This algorithm is based on Flake and Lawrence's SMO in which convex optimization problems with l variables are solved instead of standard quadratic programming problems with 2l variables where l is the number of training samples, but the strategy for working set selection is quite different. Experimental results show that the proposed algorithm is much faster than Flake and Lawrence's SMO and comparable to the fastest conventional SMO.