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Artificial Intelligence and Decision Making
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Artificial Intelligence and Decision Making, 2013, Issue 3, Pages 24–39 (Mi iipr404)  
This article is cited in 1 scientific paper (total in 1 paper)

Intelligent analysis of information

Approximation problem for factorized data

M. G. Belyaevab

a Institute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute), Moscow
b Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region
Full-text PDF (403 kB) Citations (1)
Abstract: We consider samples with factorial design of experiments (full or incomplete). Universal approximation methods don’t take into account peculiarities of such samples. We develop structural approximation method which is based on special function class and regularization. Optimal solution in this class can be found efficiently.
Keywords: nonlinear regression, factorial design of experiments, Kronecker product.
English version:
Scientific and Technical Information Processing, 2015, Volume 42, Issue 5, Pages 328–339
DOI: https://doi.org/10.3103/S0147688215050032
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: M. G. Belyaev, “Approximation problem for factorized data”, Artificial Intelligence and Decision Making, 2013, no. 3, 24–39; Scientific and Technical Information Processing, 42:5 (2015), 328–339
Citation in format AMSBIB
\Bibitem{Bel13}
\by M.~G.~Belyaev
\paper Approximation problem for factorized data
\jour Artificial Intelligence and Decision Making
\yr 2013
\issue 3
\pages 24--39
\mathnet{http://mi.mathnet.ru/iipr404}
\elib{https://elibrary.ru/item.asp?id=20277136}
\transl
\jour Scientific and Technical Information Processing
\yr 2015
\vol 42
\issue 5
\pages 328--339
\crossref{https://doi.org/10.3103/S0147688215050032}
Linking options:
  • https://www.mathnet.ru/eng/iipr404
  • https://www.mathnet.ru/eng/iipr/y2013/i3/p24
    • This publication is cited in the following 1 articles:
    1. Mikhail Belyaev, Evgeny Burnaev, Ermek Kapushev, Maxim Panov, Pavel Prikhodko, Dmitry Vetrov, Dmitry Yarotsky, “GTApprox: Surrogate modeling for industrial design”, Advances in Engineering Software, 102 (2016), 29  crossref
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    		Artificial Intelligence and Decision Making
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    Abstract page:44
    Full-text PDF :28
    References:1
     
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