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Journal of Automation and Information Sciences

Publicado 12 números por año

ISSN Imprimir: 1064-2315

ISSN En Línea: 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

Volumen 48, Edición 2, 2016, pp. 74-82
DOI: 10.1615/JAutomatInfScien.v48.i2.70
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SINOPSIS

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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