Empirically Estimable Classification Bounds Based on a Nonparametric Divergence Measure

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Authors V. Berisha, Alan Wisler, A. Hero, Andreas Spanias
Journal/Conference Name IEEE Transactions on Signal Processing
Paper Category
Paper Abstract Information divergence functions play a critical role in statistics and information theory. In this paper we show that a nonparametric f-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 distribution and for the case where there exists some mismatch between training and test distributions. We confirm these 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.
Date of publication 2016
Code Programming Language C

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