Generalized Twin Support Vector Machines

被引:1
作者
H. Moosaei
S. Ketabchi
M. Razzaghi
M. Tanveer
机构
[1] University of Bojnord,Department of Mathematics, Faculty of Science
[2] University of Guilan,Department of Applied Mathematics, Faculty of Mathematical Sciences
[3] Indian Institute of Technology Indore,Department of Mathematics
来源
Neural Processing Letters | 2021年 / 53卷
关键词
Support vector machines; Twin support vector machines; Linear programming; Unconstrained minimization problem; Generalized Newton-Armijo method; 00-01; 99-00;
D O I
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中图分类号
学科分类号
摘要
In this paper, we propose two efficient approaches of twin support vector machines (TWSVM). The first approach is to reformulate the TWSVM formulation by introducing L1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_1$$\end{document} and L∞\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_\infty $$\end{document} norms in the objective functions, and convert into linear programming problems termed as LTWSVM for binary classification. The second approach is to solve the primal TWSVM, and convert into completely unconstrained minimization problem. Since the objective function is convex, piecewise quadratic but not twice differentiable, we present an efficient algorithm using the generalized Newton’s method termed as GTWSVM. Computational comparisons of the proposed LTWSVM and GTWSVM on synthetic and several real-world benchmark datasets exhibits significantly better performance with remarkably less computational time in comparison to relevant baseline methods.
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页码:1545 / 1564
页数:19
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