On the weak convergence of the recursive orthogonal series-type kernel probabilistic neural networks in a time-varying environment

Piotr Duda; Yoichi Hayashi

Conference Proceedings

On the weak convergence of the recursive orthogonal series-type kernel probabilistic neural networks in a time-varying environment

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2012) 7203 LNCS(PART 1) 427-434

DOI: 10.1007/978-3-642-31464-3_43

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Abstract

In a paper a recursive version of general regression neural networks, based on the orthogonal series-type kernels, is presented. Sufficient conditions for convergence in probability are given assuming time-varying noise. Experimental results are provided and discussed. © 2012 Springer-Verlag.

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Duda, P., & Hayashi, Y. (2012). On the weak convergence of the recursive orthogonal series-type kernel probabilistic neural networks in a time-varying environment. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7203 LNCS, pp. 427–434). https://doi.org/10.1007/978-3-642-31464-3_43

On the weak convergence of the recursive orthogonal series-type kernel probabilistic neural networks in a time-varying environment

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