Supervised Learning Errors by Radial Basis Function Neural Networks and Regularization Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F08%3A00331008" target="_blank" >RIV/67985807:_____/08:00331008 - 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
Supervised Learning Errors by Radial Basis Function Neural Networks and Regularization Networks
Original language description
There is a gap between the theoretical results of regularization theory and practical suitability of regularization derived networks (RN). On the other hand, radial basis function networks (RBF) that can be seen as a special case of regularization networks, have a rich selection of learning algorithms. In this work we study a relationship between RN and RBF, and show that theoretical estimates for RN hold for a concrete RBF applied on real-world data.
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/GA201%2F08%2F1744" target="_blank" >GA201/08/1744: Complexity of perceptron and kernel networks</a><br>
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2008
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of Second International Conference on Future Generation Communication and Networking Symposia
ISBN
978-1-4244-3430-5
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
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Publisher name
IEEE Computer Society
Place of publication
Los Alamitos
Event location
Hainan Island
Event date
Dec 13, 2008
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000270432000079