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Mathematical Problems in Engineering
Volume 2013, Article ID 712437, 12 pages
http://dx.doi.org/10.1155/2013/712437
Research Article

Efficient Model Selection for Sparse Least-Square SVMs

1School of Mechanical and Electrical Engineering, Jiaxing University, Jiaxing 314001, China
2School of Electronics, Electrical Engineering and Computer Science, Queen's University of Belfast, Belfast BT9 5AH, UK
3School of Computer Science and IT, University of Nottingham, Nottingham NG8 1BB, UK

Received 11 April 2013; Revised 13 June 2013; Accepted 19 June 2013

Academic Editor: Ker-Wei Yu

Copyright © 2013 Xiao-Lei Xia 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

The Forward Least-Squares Approximation (FLSA) SVM is a newly-emerged Least-Square SVM (LS-SVM) whose solution is extremely sparse. The algorithm uses the number of support vectors as the regularization parameter and ensures the linear independency of the support vectors which span the solution. This paper proposed a variant of the FLSA-SVM, namely, Reduced FLSA-SVM which is of reduced computational complexity and memory requirements. The strategy of “contexts inheritance” is introduced to improve the efficiency of tuning the regularization parameter for both the FLSA-SVM and the RFLSA-SVM algorithms. Experimental results on benchmark datasets showed that, compared to the SVM and a number of its variants, the RFLSA-SVM solutions contain a reduced number of support vectors, while maintaining competitive generalization abilities. With respect to the time cost for tuning of the regularize parameter, the RFLSA-SVM algorithm was empirically demonstrated fastest compared to FLSA-SVM, the LS-SVM, and the SVM algorithms.