A method of sequentially generating a set of components of a multidimensional random variable using a nonparametric pattern recognition algorithm

We study in which way a priori information on the independence of random variables affects the approximation accuracy of a nonparametric estimate of the Rosenblatt–Parzen probability density. A new technique for generating sets of independent components of a multidimensional random variable is propo...

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Vydané v:	Kompʹûternaâ optika Ročník 45; číslo 6; s. 926 - 933
Hlavní autori:	Zenkov, I.V., Lapko, A.V., Lapko, V.A., Kiryushina, E.V., Vokin, V.N., Bakhtina, A.V.
Médium:	Journal Article
Jazyk:	English Russian
Vydavateľské údaje:	Samara National Research University 01.12.2021
Predmet:	bandwidths selection of the kernel functions forming a set of independent features hypothesis testing information processing kernel probability density estimate nonparametric pattern recognition algorithm optical data processing pattern recognition remote sensing data
ISSN:	0134-2452, 2412-6179
On-line prístup:	Získať plný text
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