Journal
PROGRESS IN NATURAL SCIENCE-MATERIALS INTERNATIONAL
Volume 18, Issue 4, Pages 475-480Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.pnsc.2007.11.012
Keywords
quantum-inspired evolutionary algorithm (QEA); parameters tuning; support vector machines (SVM); least squares support vector machines (LS-SVM)
Ask authors/readers for more resources
The most commonly used parameters selection method for support vector machines (SVM) is cross-validation, which needs a longtime 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. (C) 2007 National Natural Science Foundation of China and Chinese Academy of Sciences. Published by Elsevier Limited and Science in China Press. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available