We apply normalized RBF networks to the problem of learning nonlinear regression functions. The parameters of the networks are learned by empirical risk minimization and complexity regularization. We study convergence of the RBF networks for various radial kernels as the number of training samples increases. The rates of convergence are also examined. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Krzyzak, A., & Schäfer, D. (2008). Nonlinear function learning using radial basis function networks: Convergence and rates. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5097 LNAI, pp. 101–110). https://doi.org/10.1007/978-3-540-69731-2_11
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