Abstract:
The computational complexity of a new class of combinatorial optimization problems that are induced by optimal machine learning procedures in the class of collective piecewise linear classifiers of committee type is studied.
\Bibitem{Kha10}
\by M.~Yu.~Khachai
\paper Computational complexity of combinatorial optimization problems induced by collective procedures in machine learning
\serial Trudy Inst. Mat. i Mekh. UrO RAN
\yr 2010
\vol 16
\issue 3
\pages 276--284
\mathnet{http://mi.mathnet.ru/timm599}
\elib{https://elibrary.ru/item.asp?id=15173487}
\transl
\jour Proc. Steklov Inst. Math. (Suppl.)
\yr 2011
\vol 272
\issue , suppl. 1
\pages S46--S54
\crossref{https://doi.org/10.1134/S0081543811020040}
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https://www.mathnet.ru/eng/timm599
https://www.mathnet.ru/eng/timm/v16/i3/p276
This publication is cited in the following 2 articles:
Vl. D. Mazurov, M. I. Poberii, M. Yu. Khachai, “Ural School of Pattern Recognition: Majoritarian Approach to Ensemble Learning”, Pattern Recognit. Image Anal., 33:4 (2023), 1458
A. A. Kolokolov, T. G. Orlovskaya, “Issledovanie nekotorykh zadach tselochislennogo programmirovaniya na osnove unimodulyarnykh preobrazovanii i regulyarnykh razbienii”, Tr. IMM UrO RAN, 19, no. 2, 2013, 193–202
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