计算机工程2012，Vol.38Issue(3)：166-168,3.DOI:10.3969/j.issn.1000-3428.2012.03.056
迭代再权q范数正则化LS SVM分类算法
LS SVMs Classification Algorithm of Iterative Reweighted q-norm Regularization
刘建伟 1李双成 1罗雄麟1
作者信息
- 1. 中国石油大学自动化研究所,北京102249
- 折叠
摘要
Abstract
This paper proposes the classification algorithm of fast iterative reweighted q-norm regularization Least Squares Support Vector Machine(LS SVM). The proposed algorithm can select q value via cross-validation, where 0<q<∞, and has the characteristic of stability, quick-converging and low time complexity. In order to test the efficiency of the proposed algorithm, it is applied to three cancer datasets. Experimental results show that the presented algorithm can obtain adaptively feature selection with better generalization performance for the classification problems than LS SVM, and its training speed is much faster than LS SVM.关键词
迭代再权方法/q范数/最小二乘支持向量机/正则化/特征选择/分类算法Key words
iterative reweighted method/ q-norm/ Least Squares Support Vector Machine(LS SVM)/ regularization/ feature selection/ classification algorithm分类
信息技术与安全科学引用本文复制引用
刘建伟,李双成,罗雄麟..迭代再权q范数正则化LS SVM分类算法[J].计算机工程,2012,38(3):166-168,3.
