Advertisement

Possible Methodological Options for Development of Pattern Recognition Theory

  • Vladimir ObraztsovEmail author
  • Moqi Sun
Conference paper
First Online:
Part of the Communications in Computer and Information Science book series (CCIS, volume 1055)

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.

Keywords

Pattern recognition Metric algorithm Recognition problem without training Neural networks 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Flach, P.: Machine Learning: The Art and Science of Algorithms That Make Sense of Data, p. 400. Cambridge University Press, New York (2012)CrossRefGoogle Scholar
  2. 2.
    Kriesel, D.: A brief introduction to neural networks (2007). http://www.dkriesel.com
  3. 3.
    Goodfellow, Y., Bengio, A.: Courville deep learning. MIT Press (2016). http://www.deeplearningbook.org
  4. 4.
    Maltzev, A.I.: Algebraic systems. Science, 392 p. (1970). (in russian)Google Scholar
  5. 5.
    Zhuravlev, Ju.I.: Selected scientific papers. Magistr, 420 p. (1998). (in russian)Google Scholar
  6. 6.
    Leont’ev, V.K.: On measures of similarity and distances between objects. Comput. Math. Math. Phys. 49(11), 2041–2058 (2009)MathSciNetCrossRefGoogle Scholar
  7. 7.
    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_7CrossRefGoogle Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    Raschka, S.: Python Machine Learning, 2nd Edition, 622 p. Packt Publishing, Birmingham (2017)Google Scholar
  10. 10.
    Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Nat. Acad. Sci. 79(8), 2554–2558 (1982)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Divina, F., Marchiori, E.: Evolutionary concept learning. In: Proceedings of Genetic and Evolutionary Computation Conference, pp. 343–350 (2002)Google Scholar
  12. 12.
    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)Google Scholar
  13. 13.
    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)Google Scholar
  14. 14.
    Shut, V.O.: A model of solving of pattern recognition problems based on resolution method. Lect. Model. Simul. 14(1), 7–19 (2012)Google Scholar
  15. 15.
    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
  16. 16.
    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)CrossRefGoogle Scholar
  17. 17.
    Obraztsov, V., Sun, M.: Some methodological remarks on pattern recognition. In: Proceedings of the 14th International Conference – PRIP 2019, Minsk, pp. 350–354 (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Belarusian State UniversityMinskBelarus

Personalised recommendations

Cite paper

Buy options