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Theoretical computer science / M-estimators / Stochastic optimization / Estimation theory / Stochastic gradient descent / Markov logic network / Conditional random field / Gradient boosting / Maximum likelihood / Statistics / Machine learning / Probability and statistics


Lifted Parameter Learning in Relational Models Babak Ahmadi1 [removed] Kristian Kersting1,2,3 [removed]
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Document Date: 2012-07-18 13:16:28

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City

Sankt Augustin / Bonn / Montreal / /

Company

BP / Neural Networks / Markov Logic Networks / /

Country

Germany / United States / Canada / /

/

Facility

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

IndustryTerm

scaled conjugate gradient algorithm / lifted online training approach / connected networks / Online Training We / lifted online learning / Online max-margin weight learning / conjugate search directions / logic networks / online stochastic gradient method / incremental-dual-ascent algorithms / lifted online training / online max margin optimization problem / online training / lifted online training method / computing / propositional network / online setting / natural gradient algorithm / search direction / gradient descent algorithms / propagation algorithm / ground network / /

Organization

Knowledge Discovery Department / School of Medicine / Wake Forest University / US government / STREAM / European Commission / Institute of Geodesy and Geoinformation / University of Bonn / /

Person

Kevin P. Accelerated / Fig / Meta-Descent Stochastic / /

Position

author / /

Product

L1regularization / using L1regularization / /

ProgrammingLanguage

Python / /

PublishedMedium

Machine Learning / /

Technology

RAM / propagation algorithm / incremental-dual-ascent algorithms / Stochastic Meta-Descent Stochastic gradient descent algorithms / Machine Learning / online natural gradient algorithm / scaled conjugate gradient algorithm / /

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