The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
CMNN: cooperative modular neural networks for pattern recognition
Pattern Recognition Letters - special issue on pattern recognition in practice V
Multicategory Classification by Support Vector Machines
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
An Extended Projection Neural Network for Constrained Optimization
Neural Computation
Multi-category classification by least squares support vector regression
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A one-layer recurrent neural network for support vector machine learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new neural network for solving linear programming problems and its application
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Improved neural network for SVM learning
IEEE Transactions on Neural Networks
Efficient classification for multiclass problems using modular neural networks
IEEE Transactions on Neural Networks
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Binary classification problem can be reformulated as one optimization problem based on support vector machines and thus is well solved by one recurrent neural network (RNN). Multi-category classification problem in one-step method is then decomposed into two sub-optimization problems.In this paper, we first modify the sub-optimization problem about the bias so that its computation is reduced and its testing accuracy of classification is improved. We then propose a cooperative recurrent neural network (CRNN) for multiclass support vector machine learning. The proposed CRNN consists of two recurrent neural networks (RNNs) and each optimization problem is solved by one of the two RNNs. The proposed CRNN combines adaptively the two RNN models so that the global optimal solutions of the two optimization problems can be obtained. Furthermore, the convergence speed of the proposed CRNN is enhanced by a scaling technique. Computed results show the computational advantages of the proposed CRNN for multiclass SVM learning.