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Unifying Divergence Minimization and Statistical Inference Via Convex Duality

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83782

Altun,  Y
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Altun, Y. (2006). Unifying Divergence Minimization and Statistical Inference Via Convex Duality. Learning Theory: 19th Annual Conference on Learning Theory (COLT 2006), 139-153.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-D143-A
Zusammenfassung
In this paper 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 as a special case. Moreover, our treatment leads to stability and convergence bounds for many statistical learning problems. Finally, we show how an algorithm by Zhang can be used to solve this class of optimization problems efficiently.