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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Vydáno v:Measurement techniques Ročník 62; číslo 8; s. 665 - 672
Hlavní autoři: Lapko, A. V., Lapko, V. A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.11.2019
Springer
Springer Nature B.V
Témata:
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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Shrnutí: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 theoretical basis of the procedure under consideration is the result of a study of the asymptotic properties of multidimensional nonparametric estimates of the decision function in the problem of recognizing patterns and probability densities of the distribution of random variables in classes. The possibility of using fast procedures for choosing the blur coefficients of kernel estimates of probability densities in the synthesis of non-parametric estimates of the equation of the decision surface between classes is substantiated. The effectiveness of the proposed approach is confirmed by the results of computational experiments.
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ISSN:0543-1972
1573-8906
DOI:10.1007/s11018-019-01676-0