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Cloning for Heteroscedasticity Elimination in GMDH learning procedure
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SYSNO ASEP 0330094 Document Type A - Abstract R&D Document Type The record was not marked in the RIV R&D Document Type Není vybrán druh dokumentu Title Cloning for Heteroscedasticity Elimination in GMDH learning procedure Title Klonování pro eliminaci heteroskedasticity v učicí proceduře sítě typu GMDH Author(s) Jiřina, Marcel (UIVT-O) SAI, RID
Jiřina jr., M. (CZ)Source Title Unconventional Computation. - Berlin : Springer, 2009 / Calude C.S. ; Costa J.F. ; Dershowitz N. ; Freire E. ; Rozenberg G. - ISBN 978-3-642-03744-3
S. 288-288Number of pages 1 s. Action UC 2009. Unconventional Computation /8./ Event date 07.09.2009-11.09.2009 VEvent location Ponta Delgada Country PT - Portugal Event type WRD Language eng - English Country DE - Germany Keywords multivariate data ; GMDH ; linear regression ; Gauss-Markov conditions ; cloning ; genetic selection ; classification Subject RIV BA - General Mathematics CEZ AV0Z10300504 - UIVT-O (2005-2011) UT WOS 000272047700026 DOI 10.1007/978-3-642-03745-0_31 Annotation For the classification of multivariate data into two classes the well-known GMDH MIA (group method data handling multilayer iterative algorithm) is often used. The process of adaptation of the GMDH network is based on standard linear regression. However, it can be found that the mathematical condition of homoscedasticity for linear regression to get unbiased results is not fulfilled. We found that cloning is a simple and effective method for obtaining a less biased solution and faster convergence. Our results demonstrate that the influence of heteroscedasticity can be easily eliminated this way better behavior of GMDH algorithm can be obtained. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2010
Number of the records: 1