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Pattern Recognition and Image Analysis

, Volume 28, Issue 4, pp 688–694 | Cite as

Method of Code Description of Classes for Solving Multi-Class Problem

  • A. A. Dokukin
Mathematical Method in Pattern Recognition
  • 5 Downloads

Abstract

The method of code description of classes, which is a development of the ECOC (error-correcting output codes) method, is grounded theoretically. The main difference is as follows: a multiset of code descriptions of its training objects is used instead of one object code. It is shown that under certain conditions the two methods are equivalent. Ways for improving the recognition quality if code descriptions are used are shown.

Keywords

recognition multiclass problem ECOC multilevel method class code description 

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References

  1. 1.
    Yu. I. Zhuravlev, “Correct algebras over sets of incorrect (heuristic) algorithms. I,” Cybern. 13 (4), 489–497 (1977).MathSciNetzbMATHGoogle Scholar
  2. 2.
    Yu. I. Zhuravlev, “Correct algebras over sets of incorrect (heuristic) algorithms. II,” Cybern. 13 (6), 814–821 (1977).MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    C. Cortes and V. Vapnik, “Support–vector networks,” Mach. Learn. 20 (3), 273–297 (1995).zbMATHGoogle Scholar
  4. 4.
    V. A. Kuznetsov, O. V. Sen’ko, A. V. Kuznetsova, L. P. Semenova. A. V. Aleshchenko, T. B. Gladysheva, and A. V. Ivshina, “Recognition of fuzzy systems by the method of statistically weighted syndromes and its application for the immunohematological characterization of norm and chronic pathology,” Khim. Fizika (Chem. Phys.) 15 (1), 81–100 (1996) [in Russian].Google Scholar
  5. 5.
    T. G. Dietterich and G. Bakiri, “Solving multiclass learning problems via error–correcting output codes,” J. Artif. Intell. Res. 2, 263–286 (1995).CrossRefzbMATHGoogle Scholar
  6. 6.
    E. Allwein, R. Shapire, and Y. Singer, “Reducing multi–class to binary: A unifying approach for margin classifiers,” J. Mach. Learn. Res. 1 (1), 113–141 (2000).Google Scholar
  7. 7.
    A. A. Dokukin, V. V. Ryazanov, and O. V. Shut, “Multilevel models for solution of multiclass recognition problem,” Pattern Recogn. Image Anal.: Adv. Math. Theory Appl. 26 (3), 461–473 (2016).CrossRefGoogle Scholar
  8. 8.
    A. A. Dokukin, V. V. Ryazanov, and O. V. Shut, “Multilevel models for pattern recognition tasks with multiple classes,” Inform. Primen. (Inf. Appl.) 11 (1), 69–78 (2017).Google Scholar
  9. 9.
    M. Lichman, UCI Machine Learning Repository (University of California, School of Information and Computer Science, Irvine, CA, 2013). http://archive.ics.uci.edu/mlGoogle Scholar
  10. 10.
    K. D. Schmidt, “On the covariance of monotone functions of a random variable,” Manuscript in Dresdner Schriften zur Versicherungsmathematik (Technical University of Dresden, 2003), pp. 1–3. Available at https://www.math.tu–dresden. de/sto/schmidt/dsvm/dsvm2003–4.pdfGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  1. 1.Federal Research Center “Computer Science and Control” of the Russian Academy of SciencesMoscowRussia

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