Scaling up Machine Learning (BOK)

Ron Bekkerman

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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. 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.

Produktfakta

Språk Engelsk Engelsk Innbinding Innbundet
Utgitt 2011 Forfatter Ron Bekkerman
Forlag
CAMBRIDGE UNIVERSITY PRESS
ISBN 9780521192248
Antall sider 492 Dimensjoner 18,7cm x 26,3cm x 3,4cm
Vekt 1007 gram Leverandør Bertram Trading Ltd
Andre medvirkende John Langford, Mikhail Bilenko, Ron Bekkerman