STUDY ON PARAMETERS SELECTION OF LSSVR BASED ON GRID-DIAMOND SEARCH METHOD

摘要：

Determining the kernel function and regularization parameters for support vector machine (SVM) is very problem-dependent in practice. A popular method to deciding the kernel parameters is cross validation method. But this makes the training process time-consuming. In this paper we propose using grid-diamond search method to choose the kernel parameters. Experiment results show that the grid-diamond search method can choose proper parameters of LSSVR and CDS is the fastest algorithm among the selecting parameter while providing better simulation result.

关键词： Least Square SVR Parameters selection Diamond searching (DS) Grid search Kernel function

作者: LI-KUN HOU QING-XIN YANG

作者单位: Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability

会议类型: 国际会议

会议名称: 2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)

会议地点: 保定

会议语种:英文

页码: 1219-1224

在线出版日期: 2009-07-12（万方平台首次上网日期，不代表论文的发表时间）