Prototype sample selection based on minimization of the complete cross validation functional
M. N. Ivanov · Pattern Recognition and Image Analysis · 2010
A method of prototype sample selection from a training set for a classifier of K nearest neighbors (KNN), based on minimization of the complete cross validation functional, is proposed. The optimization leads to reduction of the training set to the minimum sufficient number of prototypes, removal (censoring) of noise samples, and improvement of the generalization ability, simultaneously.