Abstract
Brief historical background on the establishment and activities of the Institute of Control Sciences (IPU RAS). The paper presents key findings of the Institute (obtained mainly in the 20th century) in the field of pattern recognition and of related analysis of complex data. It focuses on four areas of research including (a) the method of potential functions, (b) the theory of learning and self-learning systems, (c) the generalized portrait method and recovery of dependences based on empirical data, and (d) automatic classification methods and expert classification analysis. Relations between these areas are studied. The pioneers in the field are named (M.A. Aizerman, E.M. Braverman, L.I. Rozonoer, Ya.Z. Tsypkin, V.N. Vapnik, A.Ya. Chervonenkis, I.B. Muchnik, and A.A. Dorofeyuk among others) and brief biographical notes on the life and scientific work of these scientists are presented. The follow-ups of the results thus obtained are shown. The bibliography of publications by the Institute’s researchers in leading journals of Russia on pattern recognition problems and related complex data analysis tasks is provided.
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ACKNOWLEDGMENTS
We thank D.A. Novikov and A.L. Chernyavsky for valuable recommendations and the materials that they provided.
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This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.
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A.S. Mandel, Dr. Sci. (Eng.), Chief Researcher at the Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Professor at the Department of Physics at Moscow State University. Scientific interests: random processes, operations research, theory of inventory and production management, queueing theory, control under uncertainty, reliability theory, expert-statistical systems.
A.I. Mikhalsky, Dr. Sci. (Bio.), Cand. Sci. (Eng.). Chief Researcher at the Trapeznikov Institute of Control Sciences of Russian Academy of Sciences. Scientific interests: recovery of dependences based on empirical data, mathematical and statistical modeling of morbidity and life expectancy in a changing environment, application of machine learning methods in biology, genetics and gerontology, man-machine interfaces.
Translated by D. Sventsitsky
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Mandel, A.S., Mikhalsky, A.I. On the Work of the Institute of Control Sciences of the Russian Academy of Sciences in the Field of Pattern Recognition Theory and Applications in the 20th Century. Pattern Recognit. Image Anal. 33, 1593–1623 (2023). https://doi.org/10.1134/S1054661823040284
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DOI: https://doi.org/10.1134/S1054661823040284
Keywords:
- pattern recognition
- method of potential functions
- linguistic analysis
- stochastic approximation
- learning systems
- self-learning systems
- generalized portrait method
- empirical risk
- Vapnik–Chervonenkis dimensionality
- uniform frequency convergence
- combinatorial complexity
- automatic classification
- extremal grouping
- expert classification analysis
- structural classification forecasting