Abstract
Nonparametric pattern recognition algorithms based on a randomized optimization approach are proposed. The idea of the approach is to validate the random character of fuzziness factors of kernel functions and choose parameters of their distribution law by optimizing nonparametric decision rules. Properties of the developed qualifiers are investigated. Results of their comparing with conventional nonparametric pattern recognition algorithms are analyzed.
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