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Statistical classification / Linear classifier / Expectation–maximization algorithm / Generative model / Bayesian inference / Regression analysis / Mixture model / Unsupervised learning / Gibbs sampling / Statistics / Machine learning / Supervised learning


Missing Data Problems in Machine Learning by Benjamin M. Marlin
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Document Date: 2009-06-29 08:36:54

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File Size: 2,60 MB

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City

Toronto / /

Company

Neural Networks / Feed-Forward Neural Networks / Trained LDA / Yahoo! / One Hidden Layer Neural Networks / /

Facility

Computer Science University of Toronto / /

IndustryTerm

experimental protocol / remote sensor network / particular algorithms / expectation maximization algorithms / manufacturing defects / /

Organization

University of Toronto Fellowships / Philosophy Graduate Department / University’s office of Intellectual Property / University of Toronto / /

Person

Zack Steinkamp / Todd Beaupre / Rich Zemel / Kristen Jower-Ho / Malcolm Slaney / Lauren McDonnell / Liam Stewart / Maura Rowat / Matt Fukuda / Sandra Barnat / Max Welling / Miguel CarreiraPerpinan / Sam Roweis / Josh Deinsen / Brian McGuiness / Ron Brachman / John Langford / Bruce Rowat / Mike Mull / Matt Beal / Eli Thomas / Danny Tarlow / Eric Gottschalk / David Tseng / Jenn Listgarten / Nati Srebro / Stephen Fung / David Pennock / Dennis DeCoste / Rama Natarajan / Geoff Hinton / Rus Salakhutdinov / Benjamin M. Marlin / Peter Shafton / Brendan Frey / Roland Memisevic / David Ross / Ted Meeds / Fred Zhu / Horst Samulowitz / Krisztina / /

Position

thesis supervisor / Fisher / /

PublishedMedium

Machine Learning / /

Technology

Metropolis-Hastings algorithm / Neural Network / DNA Chip / expectation maximization algorithms / Expectation Maximization algorithm / Machine Learning / 2.1 Experimental Protocols / 1.3 Experimental Protocols / /

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