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Title: Highly robust training of regularizedradial basis function networks (English)
Author: Kalina, Jan
Author: Vidnerová, Petra
Author: Janáček, Patrik
Language: English
Journal: Kybernetika
ISSN: 0023-5954 (print)
ISSN: 1805-949X (online)
Volume: 60
Issue: 1
Year: 2024
Pages: 38-59
Summary lang: English
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Category: math
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Summary: Radial basis function (RBF) networks represent established tools for nonlinear regression modeling with numerous applications in various fields. Because their standard training is vulnerable with respect to the presence of outliers in the data, several robust methods for RBF network training have been proposed recently. This paper is interested in robust regularized RBF networks. A robust inter-quantile version of RBF networks based on trimmed least squares is proposed here. Then, a systematic comparison of robust regularized RBF networks follows, which is evaluated over a set of 405 networks trained using various combinations of robustness and regularization types. The experiments proceed with a particular focus on the effect of variable selection, which is performed by means of a backward procedure, on the optimal number of RBF units. The regularized inter-quantile RBF networks based on trimmed least squares turn out to outperform the competing approaches in the experiments if a highly robust prediction error measure is considered. (English)
Keyword: regression neural networks
Keyword: robust training
Keyword: effective regularization
Keyword: quantile regression
Keyword: robustness
MSC: 62J02
MSC: 68T37
MSC: 68W25
DOI: 10.14736/kyb-2024-1-0038
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Date available: 2024-04-12T10:12:17Z
Last updated: 2024-04-12
Stable URL: http://hdl.handle.net/10338.dmlcz/152345
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