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Model theory / Statistical models / Ensemble learning / Gradient boosting / Expectation–maximization algorithm / Function / First-order logic / Regression analysis / Supervised learning / Statistics / Mathematics / Machine learning


Structure Learning with Hidden Data in Relational Domains Tushar Khot, Sriraam Natarajan∗ , Kristian Kersting∗+ , Jude Shavlik University of Wisconsin-Madison, USA ∗ Wake Forest University School of Medicine, USA
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Document Date: 2012-07-18 16:57:18


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City

J. Gradient / Neville / Laird / /

Company

MIT Press / Air Force Research Laboratory / Relational Dependency Networks / Markov Logic Networks / /

Country

United States / /

Facility

Jude Shavlik University of Wisconsin-Madison / /

IndustryTerm

gradient-tree boosting algorithm / respective algorithms / closed form solution / data augmentation algorithms / friend network / perceptron algorithms / dependency networks / greedy hill-climbing search / functionalgradient boosting algorithm / /

Organization

Royal Statistical Society / Defense Advanced Research Projects Agency / American Statistical Association / Wake Forest University School of Medicine / MIT / US government / European Commission / University of Wisconsin / /

Person

Tushar Khot / Jude Shavlik / Kristian Kersting / Sriraam Natarajan / /

Position

author / /

Product

Gibbs / C-0181 / /

ProvinceOrState

New Mexico / Wisconsin / /

PublishedMedium

Annals of Mathematics / Journal of the Royal Statistical Society / Machine Learning / Journal of the American Statistical Association / /

Technology

Bayesian structural EM algorithm / gradient-tree boosting algorithm / proposed algorithm / artificial intelligence / EM-based algorithm / CWA algorithm / proposed algorithms / functionalgradient boosting algorithm / SEM algorithm / respective algorithms / Machine Learning / data augmentation algorithms / EM algorithm / generalized EM algorithms / perceptron algorithms / /

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