High-Dimensional Approximation by Neural Networks.
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F03%3A06030182" target="_blank" >RIV/67985807:_____/03:06030182 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
High-Dimensional Approximation by Neural Networks.
Original language description
Approximation of high-dimensional mappings by neural networsk is investigated in the context of nonlinear approximation theory.It is shown that the 'curse of dimensionality' can be avoided when functions to be approximated have small special norms, whichare tailored to the type of computational units. Properties of such norms and method of derivation of their estimates are described. Estimates of rates of nonlinear approximation are applied to neural network learning formalized as approximate minimization.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
CEP classification
BA - General mathematics
OECD FORD branch
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Result continuities
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)
Others
Publication year
2003
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Advances in Learning Theory: Methods, Models and Applications.
ISBN
1-58603-341-7
Number of pages of the result
20
Pages from-to
69-88
Number of pages of the book
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Publisher name
IOS PRESS
Place of publication
Amsterdam
UT code for WoS chapter
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