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JOURNALS // Artificial Intelligence and Decision Making // Archive

Artificial Intelligence and Decision Making, 2013 Issue 3, Pages 24–39 (Mi iipr404)

This article is cited 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.


 English version:
, 2015, 42:5, 328–339

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© Steklov Math. Inst. of RAS, 2024