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Journal of Automation and Information Sciences

Выходит 12 номеров в год

ISSN Печать: 1064-2315

ISSN Онлайн: 2163-9337

SJR: 0.173 SNIP: 0.588 CiteScore™:: 2

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In-depth Classification Method on the Basis of the Mahalanobis Scalable Distance for Sets with a Priori Unequal Probabilities

Том 48, Выпуск 2, 2016, pp. 74-82
DOI: 10.1615/JAutomatInfScien.v48.i2.70
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Краткое описание

Nonparametric in-depth classification method was suggested in the case when data sets have a priori unequal probabilities and do not belong to common family of elliptic distributions. Universal in-depth classifier, which does not depend on displacement in model of location shift or violation of monotonic character of density function was developed. The Mahalanobis scalable distance is evaluated at every point with usage of the residual passage method.

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