Bounds on Rates of Variable-Basis and Neural Network Approximation.

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F01%3A06010015" target="_blank" >RIV/67985807:_____/01:06010015 - 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

Bounds on Rates of Variable-Basis and Neural Network Approximation.

Original language description

Tightness of bounds on rates of approximation by feedforward neural networks is investigated in a more general context of nonlinear approximation by variable-basis functions. Tight bounds on the worst case error in approximation by linear combinations ofn elements of an orthonormal variable basis are derived.

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%2F00%2F1482" target="_blank" >GA201/00/1482: Approximation of functions by decision trees and its application in data analysis</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

IEEE Transactions on Information Theory

ISSN

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e-ISSN

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Volume of the periodical

47

Issue of the periodical within the volume

6

Country of publishing house

US - UNITED STATES

Number of pages

7

Pages from-to

2659-2665

UT code for WoS article

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EID of the result in the Scopus database

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