Statistical testing of segment homogeneity in classification of piecewise-regular objects

Andrey V. Savchenko; Natalya S. Belova

International Journal of Applied Mathematics and Computer Science (2015)

  • Volume: 25, Issue: 4, page 915-925
  • ISSN: 1641-876X

Abstract

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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.

How to cite

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Andrey V. Savchenko, and Natalya S. Belova. "Statistical testing of segment homogeneity in classification of piecewise-regular objects." International Journal of Applied Mathematics and Computer Science 25.4 (2015): 915-925. <http://eudml.org/doc/275948>.

@article{AndreyV2015,
abstract = {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.},
author = {Andrey V. Savchenko, Natalya S. Belova},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {statistical pattern recognition; classification; testing of segment homogeneity; probabilistic neural network},
language = {eng},
number = {4},
pages = {915-925},
title = {Statistical testing of segment homogeneity in classification of piecewise-regular objects},
url = {http://eudml.org/doc/275948},
volume = {25},
year = {2015},
}

TY - JOUR
AU - Andrey V. Savchenko
AU - Natalya S. Belova
TI - Statistical testing of segment homogeneity in classification of piecewise-regular objects
JO - International Journal of Applied Mathematics and Computer Science
PY - 2015
VL - 25
IS - 4
SP - 915
EP - 925
AB - 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.
LA - eng
KW - statistical pattern recognition; classification; testing of segment homogeneity; probabilistic neural network
UR - http://eudml.org/doc/275948
ER -

References

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