Hybrid Learning of Regularization Neural Networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F10%3A00345012" target="_blank" >RIV/67985807:_____/10:00345012 - 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

Hybrid Learning of Regularization Neural Networks

Original language description

Regularization theory presents a sound framework to solving supervised learning problems. However, the regularization networks have a large size corresponding to the size of training data. In this work we study a relationship between network complexity,i.e. number of hidden units, and approximation and generalization ability. We propose an incremental hybrid learning algorithm that produces smaller networks with performance similar to original regularization networks.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

IN - Informatics

OECD FORD branch

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Project

<a href="/en/project/KJB100300804" target="_blank" >KJB100300804: Neural Networks Learning Algorithms Based on Regularization Theory</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2010

Confidentiality

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

Article name in the collection

Artificial Intelligence and Soft Computing

ISBN

978-3-642-13231-5

ISSN

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e-ISSN

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Number of pages

8

Pages from-to

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Publisher name

Springer

Place of publication

Berlin

Event location

Zakopane

Event date

Jun 13, 2010

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000281548200015