A novel LS-SVMs hyper-parameter selection based on particle swarm optimization

Authors:
X. C. Guo;J. H. Yang;C. G. Wu;C. Y. Wang;Y. C. Liang
Affiliations:
College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China and College of Sc ...;College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China;College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China and National Labo ...;College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China;College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China
Venue:
Neurocomputing
Year:
2008

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Abstract

The selection of hyper-parameters plays an important role to the performance of least-squares support vector machines (LS-SVMs). In this paper, a novel hyper-parameter selection method for LS-SVMs is presented based on the particle swarm optimization (PSO). The proposed method does not need any priori knowledge on the analytic property of the generalization performance measure and can be used to determine multiple hyper-parameters at the same time. The feasibility of this method is examined on benchmark data sets. Different kinds of kernel families are investigated by using the proposed method. Experimental results show that the best or quasi-best test performance could be obtained by using the scaling radial basis kernel function (SRBF) and RBF kernel functions, respectively.