Model Complexity of Neural Networks in High-Dimensional Approximation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F12%3A00360537" target="_blank" >RIV/67985807:_____/12:00360537 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-642-23229-9_7" target="_blank" >http://dx.doi.org/10.1007/978-3-642-23229-9_7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-642-23229-9_7" target="_blank" >10.1007/978-3-642-23229-9_7</a>
Alternative languages
Result language
angličtina
Original language name
Model Complexity of Neural Networks in High-Dimensional Approximation
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Recent Advances in Intelligent Engineering Systems
ISBN
978-3-642-23228-2
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
151-160
Publisher name
Springer
Place of publication
Berlin
Event location
Las Palmas de Gran Canaria
Event date
May 5, 2010
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000307313000007