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

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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.

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Acknowledgments

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

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Authors and Affiliations

  1. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, 110067, India

    S. Balasundaram, Deepak Gupta & Subhash Chandra Prasad

Authors
  1. S. Balasundaram
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  2. Deepak Gupta
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  3. Subhash Chandra Prasad
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Correspondence to S. Balasundaram.

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Balasundaram, S., Gupta, D. & Prasad, S.C. A new approach for training Lagrangian twin support vector machine via unconstrained convex minimization. Appl Intell 46, 124–134 (2017). https://doi.org/10.1007/s10489-016-0809-8

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  • DOI: https://doi.org/10.1007/s10489-016-0809-8

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