| 最小二乘大间隔孪生支持向量机 |
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| 引用本文： | 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. |
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| 作者姓名： | 吴青 齐韶维 孙凯悦 臧博研 赵祥 |
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| 作者单位： | 西安邮电大学 自动化学院, 西安 710121 |
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| 基金项目： | 国家自然科学基金;国家自然科学基金;国家自然科学基金;陕西省重点研发计划项目;陕西省教育厅专项科研项目 |
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| 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.
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| 关 键 词： | 最小二乘 孪生支持向量机 间隔分布 分类 |
| 收稿时间： | 2017-10-08 |
Least Squares Large Margin Twin Support Vector Machine |
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| 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 Telecommunications,2018,41(6):34-38. |
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| Authors: | WU Qing QI Shao-wei SUN Kai-yue ZANG Bo-yan ZHAO Xiang |
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| Affiliation: | School of Automation, Xi'an University of Posts and Telecommunications, Xi'an 710121, China |
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| 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. |
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| Keywords: | least squares twin support vector machine margin distribution classification |
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