Scaling up machine learning parallel and distributed approaches
Material type: TextPublication details: New York : Cambridge University Press, ©2012.Description: xvi, 475 p. ; 28 cmISBN:- 9780521192248
- 006.31 23 BEK-S
- Q325.5 .S28 2012
- COM016000
Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|---|
Books | IIITD Reference | Computer Science and Engineering | REF 006.31 BEK-S (Browse shelf(Opens below)) | Available | 003786 |
Includes bibliographical references and index.
License restrictions may limit access.
"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"--
There are no comments on this title.