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Zh. Vychisl. Mat. Mat. Fiz., 2015, Volume 55, Number 11, Pages 1959–1966 (Mi zvmmf10305)  

Some approaches to the solution of optimization problems in supervised learning

N. N. Katerinochkina

Dorodnicyn Computing Center, Russian Academy of Sciences, ul. Vavilova 40, Moscow, 119333, Russia

Abstract: There are some optimization problems that arise when highly accurate recognition algorithms are developed. One of them is to determine an optimal feasible (consistent) subsystem in a given system of linear inequalities. The optimality is defined by a number of constraints imposed on the subsystem, which can vary. Various approaches to the solution of this problem are proposed. Solution methods based on the search through the set of nodal subsystems of the given system of linear inequalities are developed. This can be exhaustive search or partial guided search that finds an approximate solution. A drastically different approximate method based on geometric considerations is proposed.

Key words: optimization, system of linear inequalities, nodal subsystem, maximum feasible subsystem.

DOI: https://doi.org/10.7868/S0044466915110083

Full text: PDF file (88 kB)
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English version:
Computational Mathematics and Mathematical Physics, 2015, 55:11, 1933–1939

Bibliographic databases:

UDC: 519.7
MSC: Primary 90C05; Secondary 68-04
Received: 03.03.2015

Citation: N. N. Katerinochkina, “Some approaches to the solution of optimization problems in supervised learning”, Zh. Vychisl. Mat. Mat. Fiz., 55:11 (2015), 1959–1966; Comput. Math. Math. Phys., 55:11 (2015), 1933–1939

Citation in format AMSBIB
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