Self-adaptive parameter optimization approach for least squares support vector machines

Authors:
Li Chun-Xiang;Zhang Wei-Min;Zhong Bi-Liang
Affiliations:
Department of Computer Science and Information Technology, Guangzhou Maritime College, Guangzhou, China;Department of Computer Science and Information Technology, Guangzhou Maritime College, Guangzhou, China;Department of Computer Science and Information Technology, Guangzhou Maritime College, Guangzhou, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

Citing 3
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Least Squares Support Vector Machine Classifiers

Neural Processing Letters
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery

Quantified Score

Hi-index	0.00

Visualization

Abstract

Based on radial basis function (RBF) kernel, a new self-adaptive method to optimize the least squares support vector machines (LS-SVM) parameters, the width of kernel parameter σ and the LS-SVM regularization parameter γ are proposed. Detailed methodology steps of this algorithm method are presented. Compared with back propagation neural networks (BPNN), various simulation experiments for nonlinear function estimation are carried out. The results show that this prediction model can achieve higher identification precision with a reasonably small size of training sample sets and has high generalization performance.