A one-layer recurrent neural network for support vector machine learning

Authors:
Youshen Xia;Jun Wang
Affiliations:
Dept. of Appl. Math., Nanjing Univ. of Posts & Telecommun., China;-
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
2004

Citing 0
Cited 15

Support vector classifiers via gradient systems with discontinuous righthand sides

Neural Networks
Support vector machine classification for large data sets via minimum enclosing ball clustering

Neurocomputing
Cooperative Recurrent Neural Network for Multiclass Support Vector Machine Learning

ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
A recurrent neural network based on projection operator for extended general variational inequalities

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Accurate fault location on EHV lines using both RBF based support vector machine and SCALCG based neural network

Expert Systems with Applications: An International Journal
An on-chip-trainable Gaussian-Kernel analog support vector machine

IEEE Transactions on Circuits and Systems Part I: Regular Papers
An Iterative Method for Deciding SVM and Single Layer Neural Network Structures

Neural Processing Letters
A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization

Neural Networks
Letters: A compact neural network for training support vector machines

Neurocomputing
Real-Time on-line-learning support vector machine based on a fully-parallel analog VLSI processor

ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Global exponential stability of discrete-time recurrent neural network for solving quadratic programming problems subject to linear constraints

Neurocomputing
Solving general convex nonlinear optimization problems by an efficient neurodynamic model

Engineering Applications of Artificial Intelligence
Generalized recurrent neural network for ε-insensitive support vector regression

Mathematics and Computers in Simulation
Biologically relevant neural network architectures for support vector machines

Neural Networks
Fast classification for large data sets via random selection clustering and Support Vector Machines

Intelligent Data Analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.