本文已被:浏览 2345次 下载 4715次
Received:November 16, 2013 Revised:December 12, 2013
Received:November 16, 2013 Revised:December 12, 2013
中文摘要: 基于高斯核的支持向量机应用很广泛,高斯核参数σ的选择对分类器性能影响很大,本文提出了从核函数性质和几何距离角度来选择参数σ,并且利用高斯函数的麦克劳林展开解决了参数σ的优化选择问题. 实验结果表明,该方法能较快地确定核函数参数σ,且SVM分类效果较好,解决了高斯核参数σ在实际应用中不易确定的问题.
Abstract:Support vector machine based on Gaussian kernel has been used in many areas. The parameter σ of the Gaussian kernel has great impact on the performance of the classifier. This paper proposes an approach to choose an optimal parameter σ based on the properties of the kernel function and the angle of geometric distance. What is more, we have solved the problem of the optimal option of the parameter σ by means of the McLaughlin expansion of the Gaussian kernel function. The experiment results indicate that this method can get parameter σ very quickly and can achieve high efficiency. Thus the difficulty of the estimation of the parameter σ can be solved by our method.
keywords: support vector machine Gaussian kernel parameter selection geometric distance McLaughlin expansion
文章编号: 中图分类号: 文献标志码:
基金项目:国家科技重大专项(2012ZX10004-301-609);国家自然科学基金(61272472,61232018,61202404);安徽省教学研究计划2010
引用文本:
王行甫,陈家伟.基于高斯核的SVM的参数选择.计算机系统应用,2014,23(7):242-245
WANG Xing-Fu,CHEN Jia-Wei.Parameter Selection of SVM with Gaussian kernel.COMPUTER SYSTEMS APPLICATIONS,2014,23(7):242-245
王行甫,陈家伟.基于高斯核的SVM的参数选择.计算机系统应用,2014,23(7):242-245
WANG Xing-Fu,CHEN Jia-Wei.Parameter Selection of SVM with Gaussian kernel.COMPUTER SYSTEMS APPLICATIONS,2014,23(7):242-245