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Solving the stochastic support vector regression with probabilistic constraints by a high-performance neural network model

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Abstract

This paper offers a recurrent neural network to support vector machine (SVM) learning in stochastic support vector regression with probabilistic constraints. The SVM is first converted into an equivalent quadratic programming (QP) formulation in linear and nonlinear cases. An artificial neural network for SVM learning is then proposed. The presented neural network framework guarantees obtaining the optimal solution of the SVM problem. The existence and convergence of the trajectories of the network are studied. The Lyapunov stability for the considered neural network is also shown. The efficiency of the proposed method is shown by three illustrative examples.

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Authors and Affiliations

  1. Faculty of Mathematical Sciences, Shahrood University of Technology, P.O. Box 3619995161-316, Shahrood, Iran

    Amir Feizi, Alireza Nazemi & Mohammad Reza Rabiei

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  1. Amir Feizi
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  2. Alireza Nazemi
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  3. Mohammad Reza Rabiei
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Correspondence to Alireza Nazemi.

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Feizi, A., Nazemi, A. & Rabiei, M.R. Solving the stochastic support vector regression with probabilistic constraints by a high-performance neural network model. Engineering with Computers 38 (Suppl 2), 1005–1020 (2022). https://doi.org/10.1007/s00366-020-01214-5

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