Learning Errors by Radial Basis Function Neural Networks and Regularization Networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F09%3A00331128" target="_blank" >RIV/67985807:_____/09:00331128 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Learning Errors by Radial Basis Function Neural Networks and Regularization Networks

Original language description

Regularization theory presents a sound framework to solving supervised learning problems. However, there is a gap between the theoretical results and practical suitability of regularization networks (RN). Radial basis function networks (RBF) that can beseen 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 to real-world data, to a certain degree. This can provide several recommendations for strategies on choosing number of units in RBF network.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

IN - Informatics

OECD FORD branch

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Project

<a href="/en/project/1M0567" target="_blank" >1M0567: Centre for Applied Cybernetics</a><br>

Continuities

Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2009

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

International Journal of Grid and Distributed Computing

ISSN

2005-4262

e-ISSN

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Volume of the periodical

1

Issue of the periodical within the volume

2

Country of publishing house

KR - KOREA, REPUBLIC OF

Number of pages

9

Pages from-to

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UT code for WoS article

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EID of the result in the Scopus database

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