TY - GEN
A1 - Savchenko, Andrey V.
A1 - Belova, Natalya S.
A2 - Iacono, Mauro - ed.
A2 - Kołodziej, Joanna - ed.
PB - Zielona Góra: Uniwersytet Zielonogórski
N2 - 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.
N2 - 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.
L1 - http://www.zbc.uz.zgora.pl/Content/79095/AMCS_2015_25_4_15.pdf
L2 - http://www.zbc.uz.zgora.pl/Content/79095
KW - statistical pattern recognition
KW - classification
KW - testing of segment homogeneity
KW - probabilistic neural network
T1 - Statistical testing of segment homogeneity in classification of piecewise-regular objects
UR - http://www.zbc.uz.zgora.pl/dlibra/docmetadata?id=79095
ER -