Comparison of Kernel Based Regularization Networks and RBF Networks
Result description
We discuss two approaches to the problem of learning from examples. They are RBF networks and regularization networks (RN). Performance of both approaches is demonstrated on experiments. We claim that the performance of RN and RBF networks are comparable.
Keywords
The result's identifiers
Result code in IS VaVaI
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Comparison of Kernel Based Regularization Networks and RBF Networks
Original language description
We discuss two approaches to the problem of learning from examples. They are RBF networks and regularization networks (RN). Performance of both approaches is demonstrated on experiments. We claim that the performance of RN and RBF networks are comparable.
Czech name
Srovnání jádrových regularizačních sítí a sítí typu RBF
Czech description
Popíšeme dva přístupy k učení příkladů, RBF sítě a regularizační sítě (RN). Chování obou a vlastnosti obou přístupů jsou předvedeny na experimentech. Experimenty ukazují, že RN a RBF sítě dosahují srovnatelných výsledku.
Classification
Type
D - Article in proceedings
CEP classification
BA - General mathematics
OECD FORD branch
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Result continuities
Project
GA201/02/0428: Nonlinear approximation with variable basis and neural networks
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2004
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Information Technologies - Applications and Theory
ISBN
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ISSN
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e-ISSN
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Number of pages
10
Pages from-to
59-68
Publisher name
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Place of publication
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Event location
Popradské Pleso
Event date
Sep 15, 2004
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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Basic information
Result type
D - Article in proceedings
CEP
BA - General mathematics
Year of implementation
2004