Some Comparisons of Model Complexity in Linear and Neural-Network Approximation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F10%3A00345940" target="_blank" >RIV/67985807:_____/10:00345940 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Some Comparisons of Model Complexity in Linear and Neural-Network Approximation
Original language description
Capabilities of linear and neural-network models are compared from the point of view of requirements on the growth of model complexity with an increasing accuracy of approximation. The bounds are formulated in terms of singular numbers of certain operators induced by computational units and high-dimensional volumes of the domains of the functions to be approximated.
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
<a href="/en/project/OC10047" target="_blank" >OC10047: Analysis of Intelligent Computational Distributed Systems</a><br>
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Artificial Neural Networks ? ICANN 2010
ISBN
978-3-642-15824-7
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
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Publisher name
Springer
Place of publication
Berlin
Event location
Thessaloniki
Event date
Sep 15, 2010
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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