Hybrid Optimization Method for Parameter Selection of Support Vector Machine

摘要：

Parameter selection of support vector machine (SVM) is a key problem in the application of SVM, which directly has influence on generalization performance of SVM. By using the support vector bound which is modified by F measure as criterion function and using genetic simulated annealing algorithm to select kernel parameters and penalty factor, a method of parameter selection of SVM based on hybrid optimization strategy is proposed in this paper. The proposed method adequately combines the optimization advantages of genetic algorithm and simulated annealing algorithms. The experiment results demonstrate that, compared with cross validation method, this proposed method improves accuracy of SVM parameter selection and generalization performance of SVM.

关键词： support vector machine genetic and simulated annealing parameter selection optimization

作者: Xiao Huaitie Feng Guoyu Song Zhiyong Chen Jianjun

作者单位: Laboratory ATR National University of Defense Technology Changsha, China

会议类型: 国际会议

会议名称: 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

会议地点: 厦门

会议语种:英文

页码: 613-616

在线出版日期: 2010-10-29（万方平台首次上网日期，不代表论文的发表时间）