title: Empirically Estimable Classification Bounds Based on a Nonparametric Divergence Measure.
abstract: Information divergence functions play a critical role in statistics and information theory. In this paper we show that a non-parametricf-divergence measure can be used to provide improved bounds on the minimum binary classification probability of error for the case when the training and test data are drawn from the same distributionandfor the case where there exists some mismatch between training and test distributions. We confirm the theoretical results by designing feature selection algorithms using the criteria from these bounds and by evaluating the algorithms on a series of pathological speech classification tasks.
authors.forename: Visar
authors.initials: V
authors.lastname: Berisha
authors.forename: Alan
authors.initials: A
authors.lastname: Wisler
authors.forename: Alfred O
authors.initials: AO
authors.lastname: Hero
authors.forename: Andreas
authors.initials: A
authors.lastname: Spanias
keywords: Bayes error rate; classification; divergence measures; domain adaptation; non-parametric divergence estimator