Institute of Information Theories and Applications FOI ITHEA
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.
* Работа выполнена при поддержке РФФИ, гранты 07-01-00331-a и 08-01-00944-a