Article ID Journal Published Year Pages File Type
417298 Computational Statistics & Data Analysis 2008 13 Pages PDF
Abstract

The bias of the empirical error rate in supervised classification is studied. It is shown that this bias can be understood as a covariance between the classification rule and the labeling of the training data. From this result, a new penalized criterion is proposed to perform model selection in classification. Applications of the resulting algorithm to simulated and real data are presented.

Related Topics
Physical Sciences and Engineering Computer Science Computational Theory and Mathematics
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