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Title: Оптимизация Оценки Вероятности Ошибочной Классификации в Дискретном Случае
Authors: Неделько, Виктор
Keywords: Pattern Recognition
Statistical Robustness
Deciding Functions
Overtraining Problem
Issue Date: 2009
Publisher: Institute of Information Theories and Applications FOI ITHEA
Abstract: The goal of the paper is to investigate what training sample estimate of misclassification probability would be the best one for the histogram classifier. Certain quality criterion is suggested. The deviation for some estimates, such as resubstitution error (empirical risk), cross validation error (leave-one-out), bootstrap and for the best estimate obtained via some optimization procedure, is calculated and compared for some examples.
Description: * Работа выполнена при поддержке РФФИ, гранты 07-01-00331-a и 08-01-00944-a
ISSN: 1313-0455
Appears in Collections:Book 08 Classification Forecasting Data Mining

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