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Computational Intelligence and Neuroscience
Volume 2013 (2013), Article ID 968438, 7 pages
http://dx.doi.org/10.1155/2013/968438
Research Article

Single Directional SMO Algorithm for Least Squares Support Vector Machines

1School of Mathematics and Statistics, Central South University, Changsha, Hunan 41007, China
2Wengjing College, Yantai University, Yantai, Shandong 264005, China
3School of Mathematics and Information Science, Yantai University, Yantai, Shandong 264005, China

Received 1 October 2012; Revised 20 December 2012; Accepted 4 January 2013

Academic Editor: Daoqiang Zhang

Copyright © 2013 Xigao Shao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Working set selection is a major step in decomposition methods for training least squares support vector machines (LS-SVMs). In this paper, a new technique for the selection of working set in sequential minimal optimization- (SMO-) type decomposition methods is proposed. By the new method, we can select a single direction to achieve the convergence of the optimality condition. A simple asymptotic convergence proof for the new algorithm is given. Experimental comparisons demonstrate that the classification accuracy of the new method is not largely different from the existing methods, but the training speed is faster than existing ones.