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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2013 Vol.3(2): 240-244 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2013.V3.311

Smoothing Technique on Linear Programming Twin Support Vector Machines

M. Tanveer
Abstract—In this paper, a new smoothing technique on linear programming TWSVM formulation is proposed whose solution is obtained by solving a pair of dual exterior penalty problems as unconstrained minimization problems using Newton method. Our approach has the advantage that a pair of matrix equation of order equals to the number of input examples is solved at each iteration of the algorithm and can be easily implemented in MATLAB without using any optimization toolbox. Computational comparisons of our proposed method against original TWSVM, GEPSVM and SVM indicate that our method is not only fast, but also shows good generalization performance.

Index Terms—Linear programming, 1-norm support vector machines, Smoothing technique, Newton method, Twin support vector machines.

M. Tanveer is with the Department of Computer Science & Engineering, The LNM Institute of Information Technology, Jaipur 302 031, India (e-mail:tanveergouri@gmail.com).

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Cite:M. Tanveer, "Smoothing Technique on Linear Programming Twin Support Vector Machines," International Journal of Machine Learning and Computing vol.3, no. 2, pp. 240-244, 2013.

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