Abstract
The article provides a fresh approach to some problems of the theory of pattern recognition. In particular, an extension variant of the distance-based models is proposed. The estimation questions of decision quality of recognition problem without training are also considered. The experiments have been conducted that show that neural networks can be considered a new standard in the solution of recognition problems.
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References
Flach, P.: Machine Learning: The Art and Science of Algorithms That Make Sense of Data, p. 400. Cambridge University Press, New York (2012)
Kriesel, D.: A brief introduction to neural networks (2007). http://www.dkriesel.com
Goodfellow, Y., Bengio, A.: Courville deep learning. MIT Press (2016). http://www.deeplearningbook.org
Maltzev, A.I.: Algebraic systems. Science, 392 p. (1970). (in russian)
Zhuravlev, Ju.I.: Selected scientific papers. Magistr, 420 p. (1998). (in russian)
Leont’ev, V.K.: On measures of similarity and distances between objects. Comput. Math. Math. Phys. 49(11), 2041–2058 (2009)
Hilpert, R.: Similarity theory. In: Durst, F. (ed.) Fluid Mechanics, pp. 193–219. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-71343-2_7
Krasnoproshin, V.V., Obraztsov, V.A.: Problems of solvability and choice of algorithms for decision making by precedence. Pattern Recogn. Image Anal. 16(2), 155–169 (2006)
Raschka, S.: Python Machine Learning, 2nd Edition, 622 p. Packt Publishing, Birmingham (2017)
Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Nat. Acad. Sci. 79(8), 2554–2558 (1982)
Divina, F., Marchiori, E.: Evolutionary concept learning. In: Proceedings of Genetic and Evolutionary Computation Conference, pp. 343–350 (2002)
Anglano, C., et al.: An experimental evaluation of coevolutive concept learning. In: Proceedings of 15th International Conference on Machine Learning, pp. 19–27. Morgan Kaufmann, San Francisco, CA (1998)
Alcalá-Fdez, J., et al.: KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J. Multiple-Valued Logic Soft Comput. 17(2–3), 255–287 (2011)
Shut, V.O.: A model of solving of pattern recognition problems based on resolution method. Lect. Model. Simul. 14(1), 7–19 (2012)
Lichman, M.: UCI machine learning repository. University of California, School of Information and Computer Science, Irvine, CA (2013). http://archive.ics.uci.edu/ml. Accessed 31 July 2015
Li, J., Wong, L.: Using rules to analyse bio-medical data: a comparison between C4.5 and PCL. In: Proceedings of Advances in Web-Age Information Management, pp. 254–265 (2003)
Obraztsov, V., Sun, M.: Some methodological remarks on pattern recognition. In: Proceedings of the 14th International Conference – PRIP 2019, Minsk, pp. 350–354 (2019)
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Obraztsov, V., Sun, M. (2019). Possible Methodological Options for Development of Pattern Recognition Theory. In: Ablameyko, S., Krasnoproshin, V., Lukashevich, M. (eds) Pattern Recognition and Information Processing. PRIP 2019. Communications in Computer and Information Science, vol 1055. Springer, Cham. https://doi.org/10.1007/978-3-030-35430-5_6
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DOI: https://doi.org/10.1007/978-3-030-35430-5_6
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