Iacono, Mauro - ed. ; Kołodziej, Joanna - ed.
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.
Zielona Góra: Uniwersytet Zielonogórski
AMCS, volume 25, number 4 (2015) ; kliknij tutaj, żeby przejść
Biblioteka Uniwersytetu Zielonogórskiego
14 lip 2025
6 maj 2024
96
https://zbc.uz.zgora.pl/repozytorium/publication/88927
| Nazwa wydania | Data |
|---|---|
| Statistical testing of segment homogeneity in classification of piecewise-regular objects | 14 lip 2025 |
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