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Parametrical discrimination

Keywords:

A.A. Moiseev – Ph.D.(Eng.), Senior Research Scientist, TECHNOS-RM (My’tishhi)
E-mail: slow.coach@yandex.ru


In situation when recognized objects characterize with parameters values aggregates parametrical discrimination is equivalent to clusterization in multidimensional space with Clark metrics. Statistical methods application allows realizing this clusterization using un-supervised learning. The results of this learning are etalons sequentially adjustable on observed values aggregates. These aggregates confronted with etalons using Fisher criterion. In case of statistical functions absence in used program package it was proposed to apply Fisher distribution’s approximation based on beta-distribution.

References:
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  6. Handbook of mathematical functions / Ed. by M. Abramovitz and I. Stegun. National bureau of standards. 1964. 1046 p.

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