Classifier quality definition on the basis of the estimation calculation approach

Authors:
Vitaliy Tayanov;Oleksiy Lutsyk
Affiliations:
Institute of Physics and Mechanics, of the National Academy of Science of Ukraine, Department of Methods and Systems of Pattern Analysis, Processing and Identification, Lviv, Ukraine;Institute of Physics and Mechanics, of the National Academy of Science of Ukraine, Department of Methods and Systems of Pattern Analysis, Processing and Identification, Lviv, Ukraine
Venue:
EC'09 Proceedings of the 10th WSEAS international conference on evolutionary computing
Year:
2009

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The nature of statistical learning theory

The nature of statistical learning theory
Bagging and Boosting for the Nearest Mean Classifier: Effects of Sample Size on Diversity and Accuracy

MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems

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Abstract

The generalized problem of classifiers training, using training sets has been considered. The accents on the principle moments, reasoning the classifiers overtraining, the ways of impact estimation of these effects degree and the methods of their control have been made. The approach, giving the possibility to estimate the effect of training set reduction on the basis of probabilistically combinatorial method has been considered. The training set reduction effect estimation gives the possibility to establish the redundancy of the training data and define the relative of the negative training objects such as spikes and spurious objects.