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Robust Training of Radial Basis Function Neural Networks
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SYSNO ASEP 0506360 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Robust Training of Radial Basis Function Neural Networks Author(s) Kalina, Jan (UIVT-O) RID, SAI, ORCID
Vidnerová, Petra (UIVT-O) RID, SAI, ORCIDSource Title Artificial Intelligence and Soft Computing. Proceedings, Part I. - Cham : Springer, 2019 / Rutkowski L. ; Scherer R. ; Korytkowski M. ; Pedrycz W. ; Tadeusiewicz R. ; Zurada J. - ISSN 0302-9743 - ISBN 978-3-030-20911-7 Pages s. 113-124 Number of pages 12 s. Publication form Print - P Action ICAISC 2019: International Conference on Artificial Intelligence and Soft Computing /18./ Event date 16.06.2019 - 20.06.2019 VEvent location Zakopane Country PL - Poland Event type WRD Language eng - English Country CH - Switzerland Keywords Machine learning ; Outliers ; Robustness ; Subset selection ; Anomaly detection Subject RIV IN - Informatics, Computer Science OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects GA19-05704S GA ČR - Czech Science Foundation (CSF) GA18-23827S GA ČR - Czech Science Foundation (CSF) Institutional support UIVT-O - RVO:67985807 UT WOS 000485150200011 EID SCOPUS 85066741931 DOI 10.1007/978-3-030-20912-4_11 Annotation Radial basis function (RBF) neural networks represent established machine learning tool with various interesting applications to nonlinear regression modeling. However, their performance may be substantially influenced by outlying measurements (outliers). Promising modifications of RBF network training have been available for the classification of data contaminated by outliers, but there remains a gap of robust training of RBF networks in the regression context. A novel robust approach based on backward subsample selection (i.e. instance selection) is proposed and presented in this paper, which searches sequentially for the most reliable subset of observations and finally performs outlier deletion. The novel approach is investigated in numerical experiments and is also applied to robustify a multilayer perceptron. The results on data containing outliers reveal the improved performance compared to conventional approaches. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2020
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