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01-01-2020 | MATHEMATICAL THEORY OF PATTERN RECOGNITION | Issue 1/2020

Pattern Recognition and Image Analysis 1/2020

Prediction Based on the Solution of the Set of Classification Problems of Supervised Learning and Degrees of Membership

Journal:
Pattern Recognition and Image Analysis > Issue 1/2020
Authors:
A. A. Lukanin, V. V. Ryazanov, N. N. Kiselyova
Important notes
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Artem Alexandrovich Lukanin. Born 1996. Graduated with a bachelor’s degree from the Department of Control and Applied Mathematics of the Moscow Institute of Physics and Technology (MIPT) in 2017. Currently finishing a master’s degree at the MIPT and is entering postgraduate education. Scientific interests: methods for optimization of recognition models, algorithms for searching and processing logical regularities of classes according to precedents, mathematical recognition models based on voting on sets of logical regularities of classes, committee synthesis of collective clustering and construction of stable solutions in clustering problems, restoring data gaps, restoring regressions from sets of recognizing algorithms, creating software classification systems, solving practical problems in medicine, technology, chemistry, and other areas.
https://static-content.springer.com/image/art%3A10.1134%2FS1054661820010095/MediaObjects/11493_2020_6053_Fig6_HTML.gif
Vladimir Vasil’evich Ryazanov. Born 1950. Graduated from the Moscow Institute of Physics and Technology in 1973. Received candidate’s degree in 1977 and doctoral degree in 1994. Full member of the Russian Academy of Natural Sciences since 1998 and Professor since 2008. Has been working at the Computing Center of the Russian Academy of Sciences since 1976. Currently the head of the department of classification methods and data analysis of the Dorodnitsyn Computing Center of the Computer Science and Management Federal Research Center of the Russian Academy of Sciences. Author of 208 papers. Scientific interests: methods for optimization of recognition models, algorithms for searching and processing logical regularities of classes according to precedents, mathematical recognition models based on voting on sets of logical regularities of classes, committee synthesis of collective clustering and construction of stable solutions in clustering problems, restoring data gaps, restoring regressions from sets of recognizing algorithms, creating software classification systems, solving practical problems in medicine, technology, chemistry, and other areas.
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Nadezhda Nikolaevna Kiselyova. Born 1949. Graduated from the Faculty of Chemistry of Moscow State University in 1971 and postgraduate program at the same faculty in 1974. Received candidate’s degree in 1975 and doctoral degree in 2004. Currently the head of the laboratory of semiconductor materials at the Baikov Institute of Metallurgy and Materials Science of the Russian Academy of Sciences. Scientific interests: computer support for design of inorganic compounds, databases of properties of inorganic substances and materials, electronic materials. Author of more than 150 papers and two monographs.
Translated by M. Chubarova

Abstract

It is proposed to use the degrees of membership of objects to each class in the process of recognition in the linear corrector model to solve the problem of restoring dependences from precedent samples. Two models of the algorithm for calculating estimates are used as classifiers. The work of the proposed model is compared with the original method and with the well-known data analysis methods. The dependence of the work of the linear corrector on its parameters is studied.

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