@misc{Savchenko_Andrey_V._Statistical,
 author={Savchenko, Andrey V. and Belova, Natalya S.},
 howpublished={online},
 publisher={Zielona Góra: Uniwersytet Zielonogórski},
 language={eng},
 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.},
 abstract={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.},
 title={Statistical testing of segment homogeneity in classification of piecewise-regular objects},
 type={artykuł},
 keywords={statistical pattern recognition, classification, testing of segment homogeneity, probabilistic neural network},
}