Unifying Divergence Minimization and Statistical Inference via Convex Duality

author: Alexander J. Smola, Machine Learning Department, Carnegie Mellon University
published: Feb. 25, 2007,   recorded: July 2006,   views: 275

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Description

We unify divergence minimization and statistical inference by means of convex duality. In the process of doing so, we prove that the dual of approximate maximum entropy estimation is maximum a posteriori estimation. Moreover, our treatment leads to stability and convergence bounds for many statistical learning problems.

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