Construction of classifiers based on kernel density estimations using the a posteriori probabilities of competing sets - Anisimov, A. V.; Galkin, O. A. (Reports of the National Academy of Sciences of Ukraine, 2015)
An approach is proposed to construct classifiers based on kernel density estimates for solving pattern recognition problems. The approach is based on the use of the a posteriori probability and a distributive π-type measure for the effective division of competing sets. The family of density estimates is applied to each set in a wide range of bandwidths for each estimate of the class density. A procedure is proposed and adapted to combine the classification results on different levels of smoothing that provides a flexible use of different bandwidths for different pairs of competing classes. Statistical uncertainties are calculated on the basis of approximate estimated probabilities of a misclassification.