Kernel Based Learning Methods: Regularization Networks and RBF Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F05%3A00339944" target="_blank" >RIV/67985807:_____/05:00339944 - 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
Kernel Based Learning Methods: Regularization Networks and RBF Networks
Original language description
We discuss two kernel based learning methods, the Regularization Networks and the RBF networks. We demonstrate the performance of both approaches on experiments. We claim that RN and RBF networks are comparable in terms of generalization error, so the RBF networks can be used as a 'cheaper' alternative.
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/1ET100300419" target="_blank" >1ET100300419: Intelligent Models, Algorithms, Methods and Tools for the Semantic Web (realization)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2005
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Deterministic and Statistical Methods in Machine Learning
ISBN
3-540-29073-7
ISSN
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e-ISSN
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Number of pages
13
Pages from-to
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Publisher name
Springer
Place of publication
Berlin
Event location
Sheffield
Event date
Sep 7, 2004
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000233290600008