Synopsis
This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.
"synopsis" may belong to another edition of this title.
About the Authors
Ron Bekkerman is a computer engineer and scientist whose experience spans across disciplines from video processing to business intelligence. Currently a senior research scientist at LinkedIn, he previously worked for a number of major companies including Hewlett-Packard and Motorola. Bekkerman's research interests lie primarily in the area of large-scale unsupervised learning. He is the corresponding author of several publications in top-tier venues, such as ICML, KDD, SIGIR, WWW,...
"About this title" may belong to another edition of this title.
- PublisherCambridge University Press
- Publication date2011
- ISBN 10 0521192242
- ISBN 13 9780521192248
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages492
- EditorBekkerman Ron, Bilenko Mikhail, Langford John
- Rating
Other Popular Editions of the Same Title
Search results for Scaling up Machine Learning: Parallel and Distributed...
Stock Image
Scaling Up Machine Learning: Parallel and Distributed Approaches
Published by
Cambridge University Press, 2011
ISBN 10: 0521192242
ISBN 13: 9780521192248
Used
Hardcover
Seller: ThriftBooks-Dallas, Dallas, TX, U.S.A.
Seller rating 5 out of 5 stars
Hardcover. Condition: As New. No Jacket. Pages are clean and are not marred by notes or folds of any kind. ~ ThriftBooks: Read More, Spend Less 2.2. Seller Inventory # G0521192242I2N00
Quantity: 1 available
Stock Image
Scaling Up Machine Learning: Parallel and Distributed Approaches
Published by
Cambridge University Press, 2011
ISBN 10: 0521192242
ISBN 13: 9780521192248
Used
Hardcover
Seller: ThriftBooks-Dallas, Dallas, TX, U.S.A.
Seller rating 5 out of 5 stars
Hardcover. Condition: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less 2.2. Seller Inventory # G0521192242I4N00
Quantity: 1 available
Stock Image
Scaling up Machine Learning Pa
Published by
Cambridge University Press, 2011
ISBN 10: 0521192242
ISBN 13: 9780521192248
Used
Hardcover
Seller: SecondSale, Montgomery, IL, U.S.A.
Seller rating 4 out of 5 stars
Condition: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc. Seller Inventory # 00068111462
Quantity: 1 available
Stock Image
Scaling up Machine Learning: Parallel and Distributed Approaches
Published by
Cambridge University Press, 2011
ISBN 10: 0521192242
ISBN 13: 9780521192248
Used
Hardcover
Seller: HPB-Red, Dallas, TX, U.S.A.
Seller rating 5 out of 5 stars
hardcover. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority! Seller Inventory # S_399948650
Quantity: 1 available
Seller Image
Scaling Up Machine Learning Parallel and Distributed Approaches
Published by
Cambridge University Press, 2011
ISBN 10: 0521192242
ISBN 13: 9780521192248
Used
Hardcover
First Edition
Seller: Boards & Wraps, Baltimore, MD, U.S.A.
Seller rating 5 out of 5 stars
Hardcover. Condition: Very Good+. Dust Jacket Condition: No Dust Jacket. First Edition. Light rubbing and toning overall and some light scratches. Interior pages clean and unmarked. A tight and clean copy. Photos upon request. International shipping billed at cost.; 4to 11" - 13" tall; 492 pages. Seller Inventory # 89027
Quantity: 1 available
Stock Image
Scaling up Machine Learning: Parallel and Distributed Approaches
Published by
Cambridge University Press, 2011
ISBN 10: 0521192242
ISBN 13: 9780521192248
New
Hardcover
Seller: DeckleEdge LLC, Albuquerque, NM, U.S.A.
Seller rating 4 out of 5 stars
Condition: new. Seller Inventory # Shelfdream0521192242
Quantity: 1 available
Stock Image
Scaling up Machine Learning: Parallel and Distributed Approaches
Published by
Cambridge University Press, 2011
ISBN 10: 0521192242
ISBN 13: 9780521192248
New
Hardcover
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # ABLIING23Feb2215580247123
Quantity: Over 20 available
Stock Image
Scaling up Machine Learning: Parallel and Distributed Approaches
Published by
Cambridge University Press, 2011
ISBN 10: 0521192242
ISBN 13: 9780521192248
New
Hardcover
Seller: California Books, Miami, FL, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # I-9780521192248
Quantity: Over 20 available
Stock Image
Scaling Up Machine Learning
Published by
Cambridge University Press, 2011
ISBN 10: 0521192242
ISBN 13: 9780521192248
New
Hardcover
Seller: Majestic Books, Hounslow, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. pp. 492 144 Illus. Seller Inventory # 5771869
Quantity: 3 available
Stock Image
Scaling up Machine Learning (Hardcover)
Published by
Cambridge University Press, Cambridge, 2011
ISBN 10: 0521192242
ISBN 13: 9780521192248
New
Hardcover
Seller: Grand Eagle Retail, Fairfield, OH, U.S.A.
Seller rating 5 out of 5 stars
Hardcover. Condition: new. Hardcover. 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. Solutions presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and online learning). Extensive coverage of parallelization of boosted trees, SVMs, spectral clustering, belief propagation and other popular learning algorithms, and deep dives into several applications, make the book equally useful for researchers, students and practitioners. In many practical situations it is impossible to run existing machine learning methods on a single computer, because either the data is too large or the speed and throughput requirements are too demanding. Researchers and practitioners will find here a variety of machine learning methods developed specifically for parallel or distributed systems, covering algorithms, platforms and applications. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780521192248
Quantity: 1 available