Model Selection for Support Vector Machines Based on Kernel Density Estimation

摘要：

This paper, aiming at overcoming the obstacles in selection of optimal kernel and its parameters, proposes a model selection approach of kernel parameters for Support Vector Machine based on kernel density estimation. By investigating kernel density estimation theory, the kernel density function based on inner product relationship of data distribution in high dimensional feature space is constructed, at the same time an evaluation function on performance of kernel mapping is established. Experimental results on both synthetic dataset and practical dataset show that proposed method is able to effectively avoid such limitations as high computational cost and process complexity in traditional model selection, and capable of optimizing kernel parameters as well as keeping better classification accuracy. So, our approach is feasible and effective.

关键词： Support Vector Machine Kernel Density Model Selection

作者: Zhu Jin Xiaoping Ma

作者单位: The School of Information and Electrical Engineering , China University of Mining & Technology , Xuzhou, Jiangsu 221116, China

会议类型: 国际会议

会议名称: The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

会议地点: 徐州

会议语种:英文

页码: 1161-1165

在线出版日期: 2010-05-26（万方平台首次上网日期，不代表论文的发表时间）