A One-Layer Recurrent Neural Network for Support Vector Machine Learning

Youshen Xia, Jun Wang

Research output: Contribution to journalArticlepeer-review

90 Citations (Scopus)


This paper presents a one-layer recurrent neural network for support vector machine (SVM) learning in pattern classification and regression. The SVM learning problem is first converted into an equivalent formulation, and then a one-layer recurrent neural network for SVM learning is proposed. The proposed neural network is guaranteed to obtain the optimal solution of support vector classification and regression. Compared with the existing two-layer neural network for the SVM classification, the proposed neural network has a low complexity for implementation. Moreover, the proposed neural network can converge exponentially to the optimal solution of SVM learning. The rate of the exponential convergence can be made arbitrarily high by simply turning up a scaling parameter. Simulation examples based on benchmark problems are discussed to show the good performance of the proposed neural network for SVM learning.

Original languageEnglish
Pages (from-to)1261-1269
Number of pages9
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Issue number2
Publication statusPublished - Apr 2004
Externally publishedYes


  • Exponential convergence
  • Quadratic programming
  • Recurrent neural networks
  • Support vector machines


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