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
Indexed in
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
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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