Solving the stochastic support vector regression with probabilistic constraints by a high-performance neural network model
作者： Amir Feizi , Alireza Nazemi , Mohammad Reza Rabiei
DOI: 10.1007/S00366-020-01214-5
关键词:
摘要: This paper offers a recurrent neural network to support vector machine (SVM) learning in stochastic regression with probabilistic constraints. The SVM is first converted into an equivalent quadratic programming (QP) formulation linear and nonlinear cases. An artificial for then proposed. presented framework guarantees obtaining the optimal solution of problem. existence convergence trajectories are studied. Lyapunov stability considered also shown. efficiency proposed method shown by three illustrative examples.
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