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Missing data / Latent Dirichlet allocation / Linear discriminant analysis / Statistics / Estimation theory / Expectation–maximization algorithm


Parallelized Variational EM for Latent Dirichlet Allocation: An Experimental Evaluation of Speed and Scalability Ramesh Nallapati, William Cohen and John Lafferty Machine Learning Department Carnegie Mellon University 50
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Document Date: 2007-09-24 14:58:31

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File Size: 98,09 KB

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City

York / /

Company

Multiprocessor LDA / Parallelized LDA / Distributed LDA / Intel / /

Country

Jordan / Haiti / /

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Facility

John Lafferty Machine Learning Department Carnegie Mellon University / /

IndustryTerm

possible using sophisticated message passing protocols / final solution / step-by-step algorithm / /

Movie

CHILDREN / /

OperatingSystem

Linux / /

Organization

Congress / Carnegie Mellon University / National Academy of Sciences / /

Person

Bennett Manigat / Ramesh Nallapati / William Cohen / John Lafferty / /

Position

Prime Minister / Public Teacher / Actor / Namphy State President / Opera Theater Actress / representative / /

Product

Ccode / /

ProgrammingLanguage

Perl / /

PublishedMedium

Proceedings of the National Academy of Sciences / Journal of Machine Learning Research / /

Technology

RAM / variational EM algorithm / Linux / detailed step-by-step algorithm / message passing protocols / 4.1 2.40GHz processor / Transmetta Efficeon TM8000 1.2GHz processor / LDA algorithm / Perl / Machine Learning / shared memory / EM algorithm / /

URL

http /

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