计算机工程与应用Issue(8)：99-102,246,5.DOI:10.3778/j.issn.1002-8331.1307-0326
双正则化参数的L2-SVM参数选择
Parameter optimization of L2-SVM with two regularization parameters
姚程宽 1许建华2
作者信息
- 1. 安庆医药高等专科学校 公共基础部，安徽 安庆 246003
- 2. 南京师范大学 计算机学院，南京 210024
- 折叠
摘要
Abstract
Searching the optimal parameters is one of the most important area of SVM and often named as parameter opti-mization or parameter selection. The L2-SVM can convert the samples into linearly separable problem. Based on the per-formance, this paper proposes the L2-SVM with two regularization parameters, and the dual formulation of L2-SVM with two regularization parameters is deduced. Combining the objective function established on minimizing the VC dimension and the gradient method, a new algorithm called Doupenalty-Gradient is present. Ten benchmark datasets are used in the experiments, and the classifying accuracy is improved obviously. The experimental results show the wonderful property and the feasibility of Doupenalty-Gradient.关键词
统计学习理论/支持向量机/VC维/参数选择Key words
statistical learning theory/support vector machines/VC dimension/parameter selection分类
信息技术与安全科学引用本文复制引用
姚程宽,许建华..双正则化参数的L2-SVM参数选择[J].计算机工程与应用,2014,(8):99-102,246,5.
