A compact neural network for training support vector machines

An analog neural network architecture for support vector machine (SVM) learning is presented in this letter, which is an improved version of a model proposed recently in the literature with additional parameters. Compared with other models, this model has several merits. First, it can solve SVMs (in the dual form) which may have multiple solutions. Second, the structure of the model enables a simple circuit implementation. Third, the model converges faster than its predecessor as indicated by empirical results.

[1]  Xiaolin Hu,et al.  Design of Recurrent Neural Networks for Solving Constrained Least Absolute Deviation Problems , 2010, IEEE Transactions on Neural Networks.

[2]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[3]  Mohamed S. Kamel,et al.  A Generalized Least Absolute Deviation Method for Parameter Estimation of Autoregressive Signals , 2008, IEEE Transactions on Neural Networks.

[4]  D. Kinderlehrer,et al.  An introduction to variational inequalities and their applications , 1980 .

[5]  Jun Wang,et al.  An Improved Algebraic Criterion for Global Exponential Stability of Recurrent Neural Networks With Time-Varying Delays , 2008, IEEE Transactions on Neural Networks.

[6]  Andrzej Cichocki,et al.  Neural networks for optimization and signal processing , 1993 .

[7]  Elisa Ricci,et al.  Analog neural network for support vector machine learning , 2006, IEEE Transactions on Neural Networks.

[8]  Jun Wang,et al.  A one-layer recurrent neural network for support vector machine learning , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[9]  John J. Hopfield,et al.  Simple 'neural' optimization networks: An A/D converter, signal decision circuit, and a linear programming circuit , 1986 .

[10]  Edgar Sanchez-Sinencio,et al.  Nonlinear switched capacitor 'neural' networks for optimization problems , 1990 .

[11]  Zhigang Zeng,et al.  Multistability of Recurrent Neural Networks With Time-varying Delays and the Piecewise Linear Activation Function , 2010, IEEE Transactions on Neural Networks.

[12]  Long Cheng,et al.  A Delayed Projection Neural Network for Solving Linear Variational Inequalities , 2009, IEEE Transactions on Neural Networks.

[13]  Xiaolin Hu,et al.  An Alternative Recurrent Neural Network for Solving Variational Inequalities and Related Optimization Problems , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[14]  Qingshan Liu,et al.  A One-Layer Recurrent Neural Network With a Discontinuous Hard-Limiting Activation Function for Quadratic Programming , 2008, IEEE Transactions on Neural Networks.

[15]  J. Hopfield,et al.  Computing with neural circuits: a model. , 1986, Science.

[16]  Yongqing Yang,et al.  Global exponential system of projection neural networks for system of generalized variational inequalities and related nonlinear minimax problems , 2010, Neurocomputing.

[17]  Yunong Zhang,et al.  A dual neural network for convex quadratic programming subject to linear equality and inequality constraints , 2002 .

[18]  Jinde Cao,et al.  A Recurrent Neural Network Based on Projection Operator for Extended General Variational Inequalities , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[19]  Haizhou Li,et al.  A Discrete-Time Neural Network for Optimization Problems With Hybrid Constraints , 2010, IEEE Transactions on Neural Networks.

[20]  Li-Zhi Liao,et al.  A New Projection-Based Neural Network for Constrained Variational Inequalities , 2009, IEEE Transactions on Neural Networks.

[21]  Youshen Xia,et al.  An Extended Projection Neural Network for Constrained Optimization , 2004, Neural Computation.

[22]  Z. Zeng,et al.  NEW PASSIVITY ANALYSIS OF CONTINUOUS-TIME RECURRENT NEURAL NETWORKS WITH MULTIPLE DISCRETE DELAYS , 2011 .

[23]  吉川 恒夫,et al.  Foundations of robotics : analysis and control , 1990 .