Neural Networks Learning as Approximate Optimization.

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F03%3A06030183" target="_blank" >RIV/67985807:_____/03:06030183 - 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

Neural Networks Learning as Approximate Optimization.

Original language description

Learning from data will be studied in the framework of approximate minimization of regularized empirical error functionals. There will be derived estimates of speed of convergence of infima achievable over approximations of an admissible set to a globalinfimum. The results will be applied to empirical error functionals regularized using stabilizers defined as squares of norms in reproducing kernel Hilbert spaces.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

BA - General mathematics

OECD FORD branch

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Project

<a href="/en/project/GA201%2F02%2F0428" target="_blank" >GA201/02/0428: Nonlinear approximation with variable basis and neural networks</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

2003

Confidentiality

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

Article name in the collection

Artificial Neural Nets and Genetic Algorithms.

ISBN

3-211-00743-1

ISSN

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e-ISSN

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Number of pages

5

Pages from-to

53-57

Publisher name

Springer-Verlag

Place of publication

Wien

Event location

Roanne [FR]

Event date

Apr 23, 2003

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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