Abstract
An algorithm to solve the least square support vector machine (LSSVM) is presented. The underlying optimization problem for LSSVM follows a system of linear equations. The proposed algorithm incorporates a fuzzy c-mean (FCM) clustering approach and the application of a recurrent neural network (RNN) to solve the system of linear equations. First, a reduced training set is obtained by the FCM clustering approach and used to train LSSVM. Then a gradient system with discontinuous righthand side, interpreted as an RNN, is designed by using the corresponding system of linear equations. The fusion of FCM clustering approach and RNN overcomes the loss of spareness of LSSVM. The efficiency of the algorithm is empirically shown on a benchmark data set generated from the University of California at Irvine (UCI) machine learning database.
This work was supported by Fund project of Heilongjiang Province Education Office (Grant No.1251112).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Suykens, J.A.K., Vandewalle, J.: Least squares support vector machine classifiers. Neural Processing Letters 9(3), 293–300 (1999)
Schölkopf, B., Smola, A.J.: Learning With Kernels. MIT Press, Cambridge (2002)
Suykens, J.A.K., Lukas, L., Vandewalle, J.: Sparse least squares support vector machine classifiers. In: roceedings of European Symposium of Artificial Neural Networks 2000, Bruges, Belgium, pp. 37–42 (April 2000)
De Kruif, B.J., De Vries, T.J.A.: Pruning error minimization in least squares support vector machines. IEEE Transactions on Neural Networks 14(3), 696–702 (2003)
Ferreira, L.V., Kaszkurewicz, E., Bhaya, A.: Solving systems of linear equations via gradient systems with discontinuous righthand sides: application to LS-SVM. IEEE Transactions on Neural Networks 16(2), 501–505 (2005)
Xue, X., Bian, W.: Subgradient-based neural networks for nonsmooth convex optimization problems. IEEE Transactions on Neural Networks Circuits and Systems I: Regular Papers 55(8), 2378–2391 (2008)
Lin, C.T., George Lee, C.S.: Neural Fuzzy Systems. Prentice-Hall, Englewood Cliffs (1996)
Suykens, J.A.K., De Brabanter, J., Lukas, L., et al.: Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing 48(1), 85–105 (2002)
Suykens, J.A.K., Vandewalle, J.: Recurrent least squares support vector machines. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 47(7), 1109–1114 (2000)
KSuykens, J.A., Lukas, L., Vandewalle, J.: Sparse approximation using least squares support vector machines. In: IEEE International Symposium on Circuits and Systems, Geneva (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, F., Wang, J., Qin, S. (2013). Training Least-Square SVM by a Recurrent Neural Network Based on Fuzzy c-mean Approach. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38715-9_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-38715-9_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38714-2
Online ISBN: 978-3-642-38715-9
eBook Packages: Computer ScienceComputer Science (R0)