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Quantum-inspired evolutionary tuning of SVM parameters

Authors :
Luo, Zhiyong
Wang, Ping
Li, Yinguo
Zhang, Wenfeng
Tang, Wei
Xiang, Min
Source :
Progress in Natural Science. Apr2008, Vol. 18 Issue 4, p475-480. 6p.
Publication Year :
2008

Abstract

Abstract: The most commonly used parameters selection method for support vector machines (SVM) is cross-validation, which needs a long-time complicated calculation. In this paper, a novel regularization parameter and a kernel parameter tuning approach of SVM are presented based on quantum-inspired evolutionary algorithm (QEA). QEA with quantum chromosome and quantum mutation has better global search capacity. The parameters of least squares support vector machines (LS-SVM) can be adjusted using quantum-inspired evolutionary optimization. Classification and function estimation are studied using LS-SVM with wavelet kernel and Gaussian kernel. The simulation results show that the proposed approach can effectively tune the parameters of LS-SVM, and the improved LS-SVM with wavelet kernel can provide better precision. [Copyright &y& Elsevier]

Details

Language :
English
ISSN :
10020071
Volume :
18
Issue :
4
Database :
Academic Search Index
Journal :
Progress in Natural Science
Publication Type :
Academic Journal
Accession number :
34303397
Full Text :
https://doi.org/10.1016/j.pnsc.2007.11.012