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Model Complexity of Neural Networks in High-Dimensional Approximation

SYSNO ASEP	0360537
Document Type	C - Proceedings Paper (int. conf.)
R&D Document Type	Conference Paper
Title	Model Complexity of Neural Networks in High-Dimensional Approximation
Author(s)	Kůrková, Věra (UIVT-O)_{RID, SAI, ORCID}
Source Title	Recent Advances in Intelligent Engineering Systems, Foundation of Computational Intelligence, 1. - Berlin : Springer, 2012 / Fodor S. ; Klempous J. ; Suárez Araujo C.P. - ISSN 1860-949X - ISBN 978-3-642-23228-2
Pages	s. 151-160
Number of pages	10 s.
Number of pages	451
Publication form	Print - P
Action	INES 2010. International Conference on Intelligent Engineering Systems /14./
Event date	05.05.2010-07.05.2010
VEvent location	Las Palmas de Gran Canaria
Country	ES - Spain
Event type	WRD
Language	eng - English
Country	DE - Germany
Keywords	model complexity of neural networks ; Gaussian radial-basis networks ; dependence on input dimension
Subject RIV	IN - Informatics, Computer Science
R&D Projects	OC10047 GA MŠMT - Ministry of Education, Youth and Sports (MEYS)
	MEB040901 GA MŠMT - Ministry of Education, Youth and Sports (MEYS)
CEZ	AV0Z10300504 - UIVT-O (2005-2011)
UT WOS	000307313000007
EID SCOPUS	82255191250
DOI	10.1007/978-3-642-23229-9_7
Annotation	The role of dimensionality in approximation by neural networks is investigated. Methods from nonlinear approximation theory are used to describe sets of functions which can be approximated by neural networks with a polynomial dependence of model complexity on the input dimension. The results are illustrated by examples of Gaussian radial networks.
Workplace	Institute of Computer Science
Contact	Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800
Year of Publishing	2013

Number of the records: 1