Continuity of Approximation by Neural Networks in Lp Spaces.

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F01%3A06010005" target="_blank" >RIV/67985807:_____/01:06010005 - 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

Continuity of Approximation by Neural Networks in Lp Spaces.

Original language description

Devices such as neural networks typically approximate the elements of some function space X by elements of a nontrivial finite union M of finite-dimensional spaces. Thus, no continuous finite neural network approximation can be within any positive constant of a best approximation in the Lp-norm.

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

BA - General mathematics

OECD FORD branch

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Project

<a href="/en/project/GA201%2F99%2F0092" target="_blank" >GA201/99/0092: Nonlinear approximation by neural networks</a><br>

Continuities

Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2001

Confidentiality

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

Name of the periodical

Annals of Operations Research

ISSN

0254-5330

e-ISSN

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Volume of the periodical

101

Issue of the periodical within the volume

1

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

5

Pages from-to

143-147

UT code for WoS article

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EID of the result in the Scopus database

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