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Approximation Bounds for Inference using Cooperative Cut

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83994

Jegelka,  S
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83814

Bilmes,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Jegelka, S., & Bilmes, J. (2011). Approximation Bounds for Inference using Cooperative Cut. In 28th International Conference on Machine Learning (ICML 2011) (pp. 577-584). Madison, WI, USA: International Machine Learning Society.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-BB16-9
Zusammenfassung
We analyze a family of probability distributions that are characterized by an embedded combinatorial structure. This family includes models having arbitrary treewidth and arbitrary sized factors. Unlike general models with such freedom, where the “most probable explanation” (MPE) problem is inapproximable, the combinatorial structure within our model, in particular the indirect use of submodularity, leads to several MPE algorithms that all have approximation guarantees.