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Applying a Reference Objects Preselection Algorithm to Real-World Data

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Abstract

The problem of optimal selection of learning objects is investigated. The effectiveness of the previously proposed iterative method for generating sets of relevant precedents is demonstrated on real-world data.

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Authors and Affiliations

  1. Faculty of Computational Mathematics and Cybernetics, Moscow State University, Moscow, Russia

    N. N. Bondarenko

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  1. N. N. Bondarenko
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Correspondence to N. N. Bondarenko.

Additional information

Nikolai Bondarenko started his scientific career with the development of mathematical models in economics, aircraft construction, and pharmaceutics. His supervisor was Yurii Ivanovich Zhuravlev, a Russian mathematician specializing in the algebraic theory of algorithms. His first publication was printed in Science Magazine (Computational Mathematics and Mathematical Physics, Springer) in 2012. In 2016, he began to work on his PhD in forecasting of large commercial structures, including classification of conditions of the structure in fixed periods of time.

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Bondarenko, N.N. Applying a Reference Objects Preselection Algorithm to Real-World Data. Pattern Recognit. Image Anal. 28, 427–429 (2018). https://doi.org/10.1134/S1054661818030045

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  • DOI: https://doi.org/10.1134/S1054661818030045

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