%0 Journal Article
%A QI Shao-wei
%A SUN Kai-yue
%A WU Qing
%A ZANG Bo-yan
%A ZHAO Xiang
%T Least Squares Large Margin Twin Support Vector Machine
%D 2018
%R 10.13190/j.jbupt.2017-180
%J Journal of Beijing University of Posts and Telecommunications
%P 34-38
%V 41
%N 6
%X 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.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2017-180