P-norm Regularized SVM Classifier by Non-convex Conjugate Gradient Algorithm

摘要：

　　Classical classification algorithm of SVM via p norm regularization usually takes p as 0,1 or 2.However, these parameters can’t always achieve the best classification results.Some scholars have discussed the situations of p∈0,1,2,where the problem is transformed into the standard quadratic programming.However,when p∈(0,1,the object is non-convex,and the method of quadratic programming is not suitable.From the point of optimization,we use Conjugate Gradient Algorithm to solve the problem.In this paper,two different kinds of SVM have been discussed and the classification results are shown by the experiments on three cancer datasets.At last,we discussed the problem of feature selection.The experiment results show that,feature selection can not only keep the precision of the prediction but also reduce model complexity.

关键词： Lp-norm 0<p<1 SVM Conjugate gradient algorithm Feature Selection

作者: ZUO Xin HUANG Hailong LI Haien LIU Jianwei

作者单位: Research Institute of Automation, China University of Petroleum, Beijing 102249

会议类型: 国际会议

会议名称: the 25th Chinese Control and Decision Conference(第25届中国控制与决策会议)

会议地点: 贵阳

会议语种:英文

页码: 2685-2690

在线出版日期: 2013-05-01（万方平台首次上网日期，不代表论文的发表时间）