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• W2908861903 abstract "From positions of the principles of training selection decomposition and collective estimation the synthesis technique of multilevel nonparametric systems of pattern recognition for the multialternate classification problem is offered. Their application provides high computing performance of information processing of big dimension. Two approaches are considered. Poorly dependent feature sets of the classified objects are in case of the former used. Considering the assumption of independence of feature sets the generalized decisive rule of maximum likelihood is under construction. The basis of the second method is made by a dichotomy method. At each its stage we form the family of the private decision functions corresponding to various feature sets of the classified objects with the subsequent their integration in the non-linear decisive rule by means of methods of nonparametric statistics. At the same time formation of the generalized decision on situation belonging to this or that class is carried out in space of values of private decision functions. The offered technique allows to use technology of parallel calculations." @default.
• W2908861903 created "2019-01-25" @default.
• W2908861903 creator A5010854688 @default.
• W2908861903 creator A5085885474 @default.
• W2908861903 creator A5090289161 @default.
• W2908861903 date "2018-10-01" @default.
• W2908861903 modified "2023-09-27" @default.
• W2908861903 title "Synthesis and the Analysis of the Nonparametric Systems of a Pattern Recognition Based on Decomposition on Dimension of the Training Selection" @default.
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• W2908861903 doi "https://doi.org/10.1109/fareastcon.2018.8602757" @default.
• W2908861903 hasPublicationYear "2018" @default.
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