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Scaling up machine learning parallel and distributed approaches

By: Bekkerman, Ron.
Contributor(s): Bekkerman, Ron | Bilenko, Mikhail | Langford, John.
Material type: materialTypeLabelBookPublisher: New York : Cambridge University Press, ©2012Description: xvi, 475 p. ; 28 cm.ISBN: 9780521192248 .Subject(s): Machine learning | Data mining | Parallel algorithms | Parallel programs (Computer programs) | COMPUTERS / Computer Vision & Pattern RecognitionCambridge Books Online - Computer ScienceSummary: "This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs, and constraints of the available options"--
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Books Books IIITD
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Computer Science and Engineering REF 006.31 BEK-S (Browse shelf) Available 003786
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Includes bibliographical references and index.

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"This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs, and constraints of the available options"--

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