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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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Scaling up machine learning: parallel and distributed approaches
2011, Cambridge University Press
in English
0521192242 9780521192248
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Includes index.
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- Created November 4, 2011
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July 14, 2023 | Edited by ImportBot | import existing book |
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November 4, 2011 | Created by LC Bot | Imported from Library of Congress MARC record |