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Machine learning / Theoretical computer science / M-estimators / Maximum likelihood / Markov random field / Statistical relational learning / Conditional random field / Stochastic gradient descent / Belief propagation / Statistics / Graphical models / Statistical models


Lifted Online Training of Relational Models with Stochastic Gradient Methods Babak Ahmadi1 , Kristian Kersting1,2,3 , and Sriraam Natarajan3 1 2
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Document Date: 2012-07-18 11:37:23

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File Size: 1,05 MB

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Bonn / /

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BP / Relational Dependency Networks / Markov Logic Networks / /

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Germany / United States / /

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Wake Forest University / Institute of Geodesy / Germany University of Bonn / /

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lifted online training approach / connected networks / conjugate search directions / free energy / incremental-dual-ascent algorithms / lifted online training / online max margin optimization problem / stochastic gradient descent algorithms / Online Training / Online Training Lifted Belief propagation / lifted online training method / computing / propositional network / online setting / gradient descent algorithms / propagation algorithm / /

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Knowledge Discovery Department / School of Medicine / Wake Forest University / Germany University of Bonn / Institute of Geodesy and Geoinformation / /

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Fig / /

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Jensen / /

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4.3 Lifted Stochastic Meta-Descent Stochastic gradient descent algorithms / propagation algorithm / incremental-dual-ascent algorithms / artificial intelligence / stochastic gradient descent algorithms / machine learning / MCMC algorithm / /

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