# 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

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topAndrey 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 -

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