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Robust Training of Radial Basis Function Neural Networks
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SYSNO ASEP 0506360 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Robust Training of Radial Basis Function Neural Networks Tvůrce(i) Kalina, Jan (UIVT-O) RID, SAI, ORCID
Vidnerová, Petra (UIVT-O) RID, SAI, ORCIDZdroj.dok. 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 Rozsah stran s. 113-124 Poč.str. 12 s. Forma vydání Tištěná - P Akce ICAISC 2019: International Conference on Artificial Intelligence and Soft Computing /18./ Datum konání 16.06.2019 - 20.06.2019 Místo konání Zakopane Země PL - Polsko Typ akce WRD Jazyk dok. eng - angličtina Země vyd. CH - Švýcarsko Klíč. slova Machine learning ; Outliers ; Robustness ; Subset selection ; Anomaly detection Vědní obor RIV IN - Informatika Obor OECD Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) CEP GA19-05704S GA ČR - Grantová agentura ČR GA18-23827S GA ČR - Grantová agentura ČR Institucionální podpora UIVT-O - RVO:67985807 UT WOS 000485150200011 EID SCOPUS 85066741931 DOI 10.1007/978-3-030-20912-4_11 Anotace 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. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2020
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