摘要
The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results and generalization ability, and now there is no systematic, general method for parameter selection. In this article, the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal paraxneter values. The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search efficiency and accuracy. Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.
The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results and generalization ability, and now there is no systematic, general method for parameter selection. In this article, the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal paraxneter values. The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search efficiency and accuracy. Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.
基金
the National Nature Science Foundation of China (60775047, 60402024)
作者简介
Yuan Xiaofang was born in 1979. He received the B.S. and M.S. degrees from Hunan University in 2001 and 2006 respectively. Now he is a Ph. D. candidate in the Hunan University. His research interests include intelligent control, neural networks, and machine learning. E-mail：yuanxiaof@21cn.comWang Yaonan was born in 1957. He received his Ph. D. degree from Hunan University in 1994. He is a professor and a doctoral supervisor in Hunan University. His research interests include control theory and applications, neural networks, and pattern recognition and intelligent image processing.
