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An improvement on parametric $$\nu $$ ν -support vector algorithm for classification

Author

Listed:
  • Saeed Ketabchi

    (University of Guilan)

  • Hossein Moosaei

    (University of Bojnord)

  • Mohamad Razzaghi

    (University of Guilan)

  • Panos M. Pardalos

    (University of Florida)

Abstract

One effective technique that has recently been considered for solving classification problems is parametric $$\nu $$ ν -support vector regression. This method obtains a concurrent learning framework for both margin determination and function approximation and leads to a convex quadratic programming problem. In this paper we introduce a new idea that converts this problem into an unconstrained convex problem. Moreover, we propose an extension of Newton’s method for solving the unconstrained convex problem. We compare the accuracy and efficiency of our method with support vector machines and parametric $$\nu $$ ν -support vector regression methods. Experimental results on several UCI benchmark data sets indicate the high efficiency and accuracy of this method.

Suggested Citation

  • Saeed Ketabchi & Hossein Moosaei & Mohamad Razzaghi & Panos M. Pardalos, 2019. "An improvement on parametric $$\nu $$ ν -support vector algorithm for classification," Annals of Operations Research, Springer, vol. 276(1), pages 155-168, May.
    • Handle: RePEc:spr:annopr:v:276:y:2019:i:1:d:10.1007_s10479-017-2724-8
    DOI: 10.1007/s10479-017-2724-8
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    References listed on IDEAS

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    1. Saeed Ketabchi & Hossein Moosaei, 2012. "Minimum Norm Solution to the Absolute Value Equation in the Convex Case," Journal of Optimization Theory and Applications, Springer, vol. 154(3), pages 1080-1087, September.
    2. Petros Xanthopoulos & Mario Guarracino & Panos Pardalos, 2014. "Robust generalized eigenvalue classifier with ellipsoidal uncertainty," Annals of Operations Research, Springer, vol. 216(1), pages 327-342, May.
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