<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

Laboratory of Algorithms and Technologies for Network Analysis, National Research University Higher School of Economics, 136 Rodionova St., Nizhny Novgorod 603093, Russia

Faculty of Computer Science, National Research University Higher School of Economics, 20 Myasnitskaya St., Moscow 101000, Russia

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The paper is focused on the problem of multi-class classification of composite (piecewise-regular) objects (e.g., speech signals, complex images, etc.). We...

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

Online veröffentlicht: 30. Dez. 2015

Seitenbereich: 915 - 925

Eingereicht: 01. Nov. 2014

DOI: https://doi.org/10.1515/amcs-2015-0065

Schlüsselwörter
statistical pattern recognition, classification, testing of segment homogeneity, probabilistic neural network

© 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.

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

Online veröffentlicht: 30. Dez. 2015

Seitenbereich: 915 - 925

Eingereicht: 01. Nov. 2014

DOI: https://doi.org/10.1515/amcs-2015-0065

Schlüsselwörterstatistical pattern recognition, classification, testing of segment homogeneity, probabilistic neural network

© 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.

Schlüsselwörter
statistical pattern recognition, classification, testing of segment homogeneity, probabilistic neural network