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International Journal of Applied Mathematics and Computer Science

Journal of University of Zielona Gora and Lubuskie Scientific Society

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Volume 25, Issue 4


Statistical Testing of Segment Homogeneity in Classification of Piecewise–Regular Objects

Andrey V. Savchenko
  • Corresponding author
  • Laboratory of Algorithms and Technologies for Network Analysis, National Research University Higher School of Economics, 136 Rodionova St., Nizhny Novgorod 603093, Russia
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/ Natalya S. Belova
  • Faculty of Computer Science, National Research University Higher School of Economics, 20 Myasnitskaya St., Moscow 101000, Russia
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Published Online: 2015-12-30 | DOI: https://doi.org/10.1515/amcs-2015-0065


The paper is focused on the problem of multi-class classification of composite (piecewise-regular) objects (e.g., speech signals, complex images, etc.). We propose a mathematical model of composite object representation as a sequence of independent segments. Each segment is represented as a random sample of independent identically distributed feature vectors. Based on this model and a statistical approach, we reduce the task to a problem of composite hypothesis testing of segment homogeneity. Several nearest-neighbor criteria are implemented, and for some of them the well-known special cases (e.g., the Kullback–Leibler minimum information discrimination principle, the probabilistic neural network) are highlighted. It is experimentally shown that the proposed approach improves the accuracy when compared with contemporary classifiers.

Keywords: statistical pattern recognition; classification; testing of segment homogeneity; probabilistic neural network


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About the article

Received: 2014-11-01

Revised: 2015-03-25

Published Online: 2015-12-30

Published in Print: 2015-12-01

Citation Information: International Journal of Applied Mathematics and Computer Science, Volume 25, Issue 4, Pages 915–925, ISSN (Online) 2083-8492, DOI: https://doi.org/10.1515/amcs-2015-0065.

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© 2015 Andrey V. Savchenko et al., published by De Gruyter Open. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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