Classifier Performance Estimation Under the Constraint of a Finite Sample Size: Resampling Schemes Applied to Neural Network Classifiers

Abstract

In a practical classifier design problem, the sample size is limited, and the available finite sample needs to be used both to design a classifier and to predict the classifier's performance for the true population. Since a larger sample is more representative of the population, it is advantageous to design the classifier with all the available cases, and… (More)
DOI: 10.1016/j.neunet.2007.12.012

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@article{Sahiner2007ClassifierPE, title={Classifier Performance Estimation Under the Constraint of a Finite Sample Size: Resampling Schemes Applied to Neural Network Classifiers}, author={Berkman Sahiner and Heang-Ping Chan and Lubomir M. Hadjiiski}, journal={2007 International Joint Conference on Neural Networks}, year={2007}, pages={1762-1766} }