Bounding the best achievable error probability for binary classification problems is relevant
to many applications including machine learning, signal processing, and information theory.
Many bounds on the Bayes binary classification error rate depend on information
divergences between the pair of class distributions. Recently, the Henze–Penrose (HP)
divergence has been proposed for bounding classification error probability. We consider the
problem of empirically estimating the HP-divergence from random samples. We derive a …

Bounding the best achievable error probability for binary classification problems is relevant
to many applications including machine learning, signal processing, and information theory.
Many bounds on the Bayes binary classification error rate depend on information
divergences between the pair of class distributions. Recently, the Henze-Penrose (HP)
divergence has been proposed for bounding classification error probability. We consider the
problem of empirically estimating the HP-divergence from random samples. We derive a …