Abstract
The SVM has been used to the nonlinear function mapping successfully, but the universal approximation property of the SVM has never been proved in theory. This paper proves the universal approximation of the SVM with RBF kernel to arbitrary functions on a compact set and deduces it to the approximation of discrete function. From simulation we can see that the RBF kernel based LS-SVM is more effective in nonlinear function estimation and can prevent the system from noise pollution, so it has high generalization ability.
Supported by a grant from the National High Technology Research and Development Program of China (863 Program) (No. 2003AA501100) and China Postdoctoral Science Foundation (No.2003034145).
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Wang, J., Chen, Q., Chen, Y. (2004). RBF Kernel Based Support Vector Machine with Universal Approximation and Its Application. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_85
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DOI: https://doi.org/10.1007/978-3-540-28647-9_85
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
Online ISBN: 978-3-540-28647-9
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