Scaling up machine learning : parallel and distributed approaches / edited by Ron Bekkerman, Mikhail Bilenko, John Langford.
Material type: TextPublication details: Cambridge ; New York : Cambridge University Press, 2012. Description: xvi, 475 p. : ill. ; 26 cmISBN: 9780521192248 (hbk.)Subject(s): Machine learning | Data mining | Parallel algorithms | Parallel programs (Computer programs) | COMPUTERS / Computer Vision & Pattern RecognitionDDC classification: 006.3/1 Other classification: COM016000 Summary: "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"--Item type | Current library | Call number | Status | Date due | Barcode |
---|---|---|---|---|---|
REFERENCE | Malaviya National Institute of Technology Reference | 006.3/1 BEK (Browse shelf(Opens below)) | Not for loan | 84654 |
Includes bibliographical references and index.
"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.