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Highly robust training of regularizedradial basis function networks

Jan Kalina, Petra Vidnerová, Patrik Janáček (2024)

Kybernetika

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...

How to deal with regression models with a weak nonlinearity

Eva Tesaríková, Lubomír Kubáček (2001)

Discussiones Mathematicae Probability and Statistics

If a nonlinear regression model is linearized in a non-sufficient small neighbourhood of the actual parameter, then all statistical inferences may be deteriorated. Some criteria how to recognize this are already developed. The aim of the paper is to demonstrate the behaviour of the program for utilization of these criteria.

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