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Continuity of Approximation by Neural Networks in Lp Spaces.

The result's identifiers

  • 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

  • DOI - Digital Object Identifier

Alternative languages

  • 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

  • Czech description

Classification

  • Type

    J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

  • CEP classification

    BA - General mathematics

  • OECD FORD branch

Result continuities

  • 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)

Others

  • Publication year

    2001

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Annals of Operations Research

  • ISSN

    0254-5330

  • e-ISSN

  • 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

  • EID of the result in the Scopus database