最小二乘大间隔孪生支持向量机

doi:10.13190/j.jbupt.2017-180

北京邮电大学学报 ›› 2018, Vol. 41 ›› Issue (6): 34-38.doi: 10.13190/j.jbupt.2017-180

最小二乘大间隔孪生支持向量机

吴青, 齐韶维, 孙凯悦, 臧博研, 赵祥

西安邮电大学自动化学院, 西安 710121

收稿日期:2017-10-08 出版日期:2018-12-28 发布日期:2018-12-24
作者简介:吴青(1975-),女,教授,硕士生导师,E-mail:xiyouwuq@126.com.
基金资助:
国家自然科学基金项目（51875457，61472307，51405387）；陕西省重点研发计划项目（2018GY-018）；陕西省教育厅专项科研项目（17JK0713）

Least Squares Large Margin Twin Support Vector Machine

WU Qing, QI Shao-wei, SUN Kai-yue, ZANG Bo-yan, ZHAO Xiang

School of Automation, Xi'an University of Posts and Telecommunications, Xi'an 710121, China

Received:2017-10-08 Online:2018-12-28 Published:2018-12-24

摘要/Abstract

摘要： 针对最小二乘孪生支持向量机（LSTWSVM）精度较低和可能存在的"奇异性"问题，提出了一种最小二乘大间隔孪生支持向量机（LSLMTSVM）.该算法在最小二乘孪生支持向量机的优化目标函数中引入了间隔分布，提高了算法的泛化性能.在目标函数中加入正则项，实现了结构风险最小化，进一步提高了分类能力.实验结果表明，最小二乘大间隔孪生支持向量机比已有的相关算法性能更优.

关键词: 最小二乘, 孪生支持向量机, 间隔分布, 分类

Abstract: In order to overcome low accuracy and possible singularity of least squares twin support vector machine (LSTWSVM), a least squares large margin twin support vector machine (LSLMTSVM) is presented. The proposed algorithm improves generalization performance by introducing margin distribution to the optimization objective function of the LSTWSVM. Additionally, the structural risk minimization principle is implemented by adding the regularization term to the objective function which improves classification ability. Experimental results show that LSLMTSVM has better classification performance than the existing algorithm.

Key words: least squares, twin support vector machine, margin distribution, classification

中图分类号:

TN181

吴青, 齐韶维, 孙凯悦, 臧博研, 赵祥. 最小二乘大间隔孪生支持向量机[J]. 北京邮电大学学报, 2018, 41(6): 34-38.

WU Qing, QI Shao-wei, SUN Kai-yue, ZANG Bo-yan, ZHAO Xiang. Least Squares Large Margin Twin Support Vector Machine[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2018, 41(6): 34-38.

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最小二乘大间隔孪生支持向量机

Least Squares Large Margin Twin Support Vector Machine

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