双正则化参数的L_2-SVM参数选择  

Parameter optimization of L_2-SVM with two regularization parameters

作　　者：姚程宽[1] 许建华[2] 

机构地区：[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

出　　处：《计算机工程与应用》2014年第8期99-102,246,共5页Computer Engineering and Applications

摘　　要：寻找支持向量机(SVM)的最优参数是支持向量机研究领域的热点之一。2范数软间隔SVM(L2-SVM)将样本转化成线性可分,在原始单正则化参数L2-SVM的基础上,提出双正则化参数的L2-SVM,获得它的对偶形式,从而确定了最优化的目标函数。然后结合梯度法,提出了一种新的支持向量机参数选择的新方法(Doupenalty-Gradient)。实验使用了10个基准数据集,结果表明,Doupenalty-Gradient方法是可行且有效的。对于实验所用的样本,极大地改善了分类精度。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 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]