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:
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
Resource Type:
DOI:
Pages:
Source:
AMCS, volume 25, number 4 (2015) ; click here to follow the link