自动化学报2012，Vol.38Issue(1)：76-87,12.DOI:10.3724/SP.J.1004.2012.00076
p范数正则化支持向量机分类算法
Classification Algorithm of Support Vector Machine via p-norm Regularization
摘要
Abstract
The L2 penalty support vector machine (SVM) algorithm is one of the most widely used learning algorithms, meanwhile L1 norm and L0 norm penalty support vector machines have been devised, which achieve simultaneously feature selection and classifier construction. However, in both methods, the regularization parameter is predetermined, I.e., the default p = 2 or p = 1. Our experimental study shows that different data, using a different regularization of order, can improve prediction accuracy of the classification algorithm. In this paper, new classifier design pattern of SVM based on p-norm regularization is proposed, where 0 < p < 2. We design grid method to select parameter values of model, use the iterative reweighted method to solve classification object function then discover the right parameter values of model at the minimum prediction error. The performance of classification and feature selection on real datasets indicate that the devised algorithm is better than L2-norm, L1-norm, and L0-norm SVM.关键词
迭代再权方法/p范数(0＜p≤2)/支持向量机/特征选择/稀疏化模型/高维小样本数据Key words
Iterative reweighted method/p-norm (0 < p < 2)/support vector machine (SVM)/feature selection/sparse model/high-dimensional small sample dataset引用本文复制引用
刘建伟,李双成,罗雄麟..p范数正则化支持向量机分类算法[J].自动化学报,2012,38(1):76-87,12.基金项目
国家自然科学基金(21006127,20976193),中国石油大学(北京)基础学科研究基金项目资助 （21006127,20976193）
