<I>p</I>范数正则化支持向量机分类算法

刘建伟; 李双成; 罗雄麟

doi:10.3724/SP.J.1004.2012.00076

p范数正则化支持向量机分类算法

doi: 10.3724/SP.J.1004.2012.00076

1.
中国石油大学自动化研究所北京 102249

详细信息

通讯作者:
刘建伟中国石油大学(北京)地球物理与信息技术学院自动化系副研究员. 主要研究方向为智能信息处理,复杂系统分析,预测与控制,算法分析与设计.本文通信作者. E-mail: liujw@cup.edu.cn

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出版历程
- 收稿日期: 2010-12-24
- 修回日期: 2011-08-30
- 刊出日期: 2012-01-20

Classification Algorithm of Support Vector Machine via p-norm Regularization

1.
Research Institute of Automation, China University of Petroleum, Beijing 102249

摘要

摘要: L2范数罚支持向量机(Support vector machine,SVM)是目前使用最广泛的分类器算法之一,同时实现特征选择和分类器构造的L1范数和L0范数罚SVM算法也已经提出.但是,这两个方法中,正则化阶次都是事先给定,预设p=2或p=1.而我们的实验研究显示,对于不同的数据,使用不同的正则化阶次,可以改进分类算法的预测准确率.本文提出p范数正则化SVM分类器算法设计新模式,正则化范数的阶次p可取范围为02范数罚SVM,L1范数罚SVM和L0范数罚SVM.
- 迭代再权方法 /
- p范数(0 /
- 支持向量机 /
- 特征选择 /
- 稀疏化模型 /
- 高维小样本数据
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 02-norm, L1-norm, and L0-norm SVM.
- Iterative reweighted method /
- p-norm (0 /
- support vector machine (SVM) /
- feature selection /
- sparse model /
- high-dimensional small sample dataset

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[1]

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