Scaling Up Machine Learning Parallel and Distributed Approaches

Bekkerman, Ron & Mikhail Bilenko & John Langford

ISBN 10: 0521192242 / ISBN 13: 9780521192248

Published by Cambridge University Press, 2011

About this Item

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

Bibliographic Details

Title: Scaling Up Machine Learning Parallel and ...

Publisher: Cambridge University Press

Publication Date: 2011

Binding: Hardcover

Condition: Very Good+

Dust Jacket Condition: No Dust Jacket

Edition: First Edition.

About this title

Synopsis:

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.

Book Description:

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.