Methods for Rapid Selection of Kernel Function Blur Coefficients in a Nonparametric Pattern Recognition Algorithm

A fast algorithm is proposed for choosing the coefficients of blur coefficients for kernel functions in a nonparametric estimate of the separating surface equation for a two-alternative pattern recognition problem. The algorithm is based on the results of a study of the asymptotic properties of nonp...

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Veröffentlicht in:	Measurement techniques Jg. 62; H. 4; S. 300 - 306
Hauptverfasser:	Lapko, A. V., Lapko, V. A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.07.2019 Springer Springer Nature B.V
Schlagworte:	Algorithms Analysis Analytical Chemistry Asymptotic properties Characterization and Evaluation of Materials Coefficients Distribution (Probability theory) General Problems of Metrology and Measurement Technique Kernel functions Measurement Science and Instrumentation Methods Nonparametric statistics Pattern recognition Physical Chemistry Physics Physics and Astronomy Random variables kernel probability density estimation nonparametric pattern recognition algorithm Rosenblatt-Parzen probability density estimate quick selection of blur coefficients
ISSN:	0543-1972, 1573-8906
Online-Zugang:	Volltext
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