Efficient model selection for sparse least-square SVMs

Impact Factor:1.082

DOI number:10.1155/2013/712437

Affiliation of Author(s):Univ Nottingham

Teaching and Research Group:Sch Comp Sci & IT, Nottingham NG8 1BB

Journal:Mathematical Problems in Engineering

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.

Co-author:Xiaolei Xia,Suxiang Qian,Xueqin Liu,Huanlai Xing

Document Code:10.1155/2013/712437

Volume:2013

ISSN No.:1024-123X

Translation or Not:no

Date of Publication:2013-08-21