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A New Method for Classifying Random Variables Based on Support Vector Machine

Author

Listed:
  • Maryam Abaszade

    (Ferdowsi University of Mashhad)

  • Sohrab Effati

    (Ferdowsi University of Mashhad)

Abstract

In this paper, a new version of Support Vector Machine (SVM) is proposed which any of training samples are considered the random variables. Hence, in order to achieve robustness, the constraint in SVM must be replaced with probability of constraint. In this new model, by applying the nonparametric statistical methods, we obtain the optimal separating hyperplane by solving a quadratic optimization problem. Afterwards, we present the least squares model of our proposed method. The efficiency of our proposed method is shown by several examples for both cases (linear and nonlinear) with probabilistic constraints.

Suggested Citation

  • Maryam Abaszade & Sohrab Effati, 2019. "A New Method for Classifying Random Variables Based on Support Vector Machine," Journal of Classification, Springer;The Classification Society, vol. 36(1), pages 152-174, April.
    • Handle: RePEc:spr:jclass:v:36:y:2019:i:1:d:10.1007_s00357-018-9282-x
    DOI: 10.1007/s00357-018-9282-x
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    References listed on IDEAS

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    1. Trafalis, Theodore B. & Gilbert, Robin C., 2006. "Robust classification and regression using support vector machines," European Journal of Operational Research, Elsevier, vol. 173(3), pages 893-909, September.
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    Cited by:

    1. Douglas L. Steinley, 2019. "Editorial: Journal of Classification Vol. 36-3," Journal of Classification, Springer;The Classification Society, vol. 36(3), pages 393-396, October.

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