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Applied Intelligence

, Volume 46, Issue 1, pp 124–134 | Cite as

A new approach for training Lagrangian twin support vector machine via unconstrained convex minimization

  • S. Balasundaram
  • Deepak Gupta
  • Subhash Chandra Prasad
Article

Abstract

In this paper, a novel unconstrained convex minimization problem formulation for the Lagrangian dual of the recently introduced twin support vector machine (TWSVM) in simpler form is proposed for constructing binary classifiers. Since the objective functions of the modified minimization problems contain non-smooth ‘plus’ function, we solve them by Newton iterative method either by considering their generalized Hessian matrices or replacing the ‘plus’ function by a smooth approximation function. Numerical experiments were performed on a number of interesting real-world benchmark data sets. Computational results clearly illustrates the effectiveness and the applicability of the proposed approach as comparable or better generalization performance with faster learning speed is obtained in comparison with SVM, least squares TWSVM (LS-TWSVM) and TWSVM.

Keywords

Generalized Hessian approach Smooth approximation formulation Twin support vector machine 

Notes

Acknowledgments

The authors are thankful to the anonymous reviewers for their comments.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • S. Balasundaram
    • 1
  • Deepak Gupta
    • 1
  • Subhash Chandra Prasad
    • 1
  1. 1.School of Computer and Systems SciencesJawaharlal Nehru UniversityNew DelhiIndia

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