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Construction and evaluation of classifiers for forensic document analysis
In this study we illustrate a statistical approach to questioned document
examination. Specifically, we consider the construction of three classifiers
that predict the writer of a sample document based on categorical data. To
evaluate these classifiers, we use a data set with a large number of writers
and a small number of writing samples per writer. Since the resulting
classifiers were found to have near perfect accuracy using leave-one-out
cross-validation, we propose a novel Bayesian-based cross-validation method for
evaluating the classifiers.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS379 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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