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Learning and convergence of the normalized radial basis functions networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2018) 10841 LNAI 118-129
DOI: 10.1007/978-3-319-91253-0_12
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Abstract

In the paper we analyze convergence and rates of convergence of the normalized radial basis function networks by relating their L2 error to the L2 error of the Wolverton-Wagner regression estimate. The network parameters are learned by minimizing the empirical risk and are applied in function learning and classification.

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Krzyżak, A., & Partyka, M. (2018). Learning and convergence of the normalized radial basis functions networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10841 LNAI, pp. 118–129). Springer Verlag. https://doi.org/10.1007/978-3-319-91253-0_12

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