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Artificial Intelligence and Decision Making, 2013, Issue 3, Pages 24–39
(Mi iipr404)
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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
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.
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
Linking options:
https://www.mathnet.ru/eng/iipr404 https://www.mathnet.ru/eng/iipr/y2013/i3/p24
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Statistics & downloads: |
Abstract page: | 30 | Full-text PDF : | 11 | References: | 1 |
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