Creator:

Savchenko, Andrey V. ; Belova, Natalya S.

Contributor:

Iacono, Mauro - ed. ; Kołodziej, Joanna - ed.

Title:

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

Subtitle:

.

Group publication title:

AMCS, Volume 25 (2015)

Subject and Keywords:

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

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.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Date:

2015

Resource Type:

artykuł

DOI:

10.1515/amcs-2015-0065

Pages:

915-925

Source:

AMCS, volume 25, number 4 (2015) ; click here to follow the link

Language:

eng

License CC BY 4.0:

click here to follow the link

Rights:

Biblioteka Uniwersytetu Zielonogórskiego