Fast Selection of Blur Coefficients in a Multidimensional Nonparametric Pattern Recognition Algorithm
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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.
Keywordsnonparametric pattern recognition algorithm maximum likelihood criterion multidimensional kernel probability density estimate quick choice of blur coefficients asymptotic properties of probability density estimates of Rosenblatt Parzen type
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