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Radial basis function networks and nonparametric classification: complexity regularization and rates of convergence

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2 Author(s)
Krzyzak, A. ; Dept. of Comput. Sci., Concordia Univ., Montreal, Que., Canada ; Linder, T.

The method of complexity regularization is applied to one hidden-layer radial basis function networks to derive regression estimation bounds and convergence rates for classification. Bounds on the expected risk in terms of the training sample size are obtained for a large class of activation functions, namely functions of bounded variation. Rates of convergence to the optimal loss are also derived

Published in:

Pattern Recognition, 1996., Proceedings of the 13th International Conference on  (Volume:4 )

Date of Conference:

25-29 Aug 1996