| 迭代再权q范数正则化LS SVM分类算法 |
| |
| 引用本文： | 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. |
| |
| 作者姓名： | 刘建伟 李双成 罗雄麟 |
| |
| 作者单位： | 中国石油大学自动化研究所,北京,102249 |
| |
| 摘 要： | 提出一种迭代再权q范数正则化最小二乘支持向量机(LS SVM)分类算法。该算法通过交叉校验过程选择正则化范数的阶次q (0
|
| 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 |
| 收稿时间： | 2011-07-21 |
LS SVMs Classification Algorithm of Iterative Reweighted q-norm Regularization |
| |
| LIU Jian-wei
,
LI Shuang-cheng
,
LUO Xiong-lin.LS SVMs Classification Algorithm of Iterative Reweighted q-norm Regularization[J].Computer Engineering,2012,38(3):166-168. |
| |
| Authors: | LIU Jian-wei LI Shuang-cheng LUO Xiong-lin |
| |
| Affiliation: | (Research Institute of Automation, China University of Petroleum, Beijing 102249, China) |
| |
| 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 O〈q〈oc,, 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. |
| |
| Keywords: | iterative reweighted method q-norm Least Squares Support Vector Machine（LS SVM） regularization feature selection classification algorithm |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
| 点击此处可从《计算机工程》浏览原始摘要信息 |
|
点击此处可从《计算机工程》下载全文 |
|
