Robust Training of Radial Basis Function Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F19%3A00506360" target="_blank" >RIV/67985807:_____/19:00506360 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-030-20912-4_11" target="_blank" >http://dx.doi.org/10.1007/978-3-030-20912-4_11</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-20912-4_11" target="_blank" >10.1007/978-3-030-20912-4_11</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Robust Training of Radial Basis Function Neural Networks

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Robust Training of Radial Basis Function Neural Networks

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Artificial Intelligence and Soft Computing. Proceedings, Part I

ISBN

978-3-030-20911-7

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

12

Strana od-do

113-124

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Zakopane

Datum konání akce

16. 6. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000485150200011