Fast Selection of Blur Coefficients in a Multidimensional Nonparametric Pattern Recognition Algorithm

A fast procedure is proposed for choosing the blur coefficients of kernel functions in a multidimensional nonparametric estimation of the equation of a decision surface for a two-alternative problem of pattern recognition. The decision classification rule meets the maximum likelihood criterion. The...

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Vydané v:	Measurement techniques Ročník 62; číslo 8; s. 665 - 672
Hlavní autori:	Lapko, A. V., Lapko, V. A.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.11.2019 Springer Springer Nature B.V
Predmet:	Algorithms Analysis Analytical Chemistry Asymptotic properties Characterization and Evaluation of Materials Coefficients Decision theory Distribution (Probability theory) Estimates General Problems of Metrology and Measurement Technique Kernel functions Measurement Science and Instrumentation Nonparametric statistics Pattern recognition Physical Chemistry Physics Physics and Astronomy Random variables Parzen type nonparametric pattern recognition algorithm multidimensional kernel probability density estimate maximum likelihood criterion quick choice of blur coefficients asymptotic properties of probability density estimates of Rosenblatt
ISSN:	0543-1972, 1573-8906
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