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 nonparametric estimates of the decision function in the recognition problem for patterns and the probability densities of the distribution of random variables in classes. We compare the proposed algorithm with the traditional approach based on minimizing the estimated probability of a classification error.
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This study was carried out with the financial support of the Russian Foundation for Basic Research in the framework of the research project No. 18-01-00251.
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Translated from Izmeritel’naya Tekhnika, No. 4, pp. 4–8, April, 2019.
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Lapko, A.V., Lapko, V.A. Methods for Rapid Selection of Kernel Function Blur Coefficients in a Nonparametric Pattern Recognition Algorithm. Meas Tech 62, 300–306 (2019). https://doi.org/10.1007/s11018-019-01621-1
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DOI: https://doi.org/10.1007/s11018-019-01621-1