Parameter selection of support vector regression based on hybrid optimization algorithm and its application
Xin WANG
Chunhua YANG
Bin QIN
Weihua GUI
摘要：Choosing optimal parameters for support vector regression (SVR) is an important step in SVR design, which strongly affects the performance of SVR. In this paper, based on the analysis of influence of SVR parameters on generalization error,a new approach with two steps is proposed for selecting SVR parameters. First the kernel function and SVM parameters are optimized roughly through genetic algorithm, then the kernel parameter is finely adjusted by local linear search. This approach has been successfully applied to the prediction model of the sulfur content in hot metal. The experiment results show that the proposed approach can yield better generalization performance of SVR than other methods.
机标关键词：hybrid optimization algorithmsupport vector regressiongeneralization erroroptimal parametersgenetic algorithmprediction modelkernel functionsulfur content
分类号：O1(数学)
资助基金：the National Natural Science Foundation of China(60574030)The National Basic Research Program of China(2002CB312203)湖南省教育厅科研项目(05C523)
论文发表日期：2005-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：6( 371-376 )
英文信息