Scaling up Machine Learning: Parallel and Distributed Approaches

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9780521192248: Scaling up Machine Learning: Parallel and Distributed Approaches

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.

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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.

About the Author:

Dr 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, IJCAI, CVPR, EMNLP and JMLR.

Dr Mikhail Bilenko is a researcher in the Machine Learning and Intelligence group at Microsoft Research. His research interests center on machine learning and data mining tasks that arise in the context of large behavioral and textual datasets. Bilenko's recent work has focused on learning algorithms that leverage user behavior to improve online advertising. His papers have been published at KDD, ICML, SIGIR, and WWW among other venues, and he has received best paper awards from SIGIR and KDD.

Dr John Langford is a computer scientist working as a senior researcher at Yahoo! Research. Previously, he was affiliated with the Toyota Technological Institute and IBM T. J. Watson Research Center. Langford's work has been published at conferences and in journals including ICML, COLT, NIPS, UAI, KDD, JMLR and MLJ. He received the Pat Goldberg Memorial Best Paper Award, as well as best paper awards from ACM EC and WSDM. He is also the author of the popular machine learning weblog, hunch.net.

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Ron Bekkerman (Editor), Mikhail Bilenko (Editor), John Langford (Editor)
Editore: Cambridge University Press (2011)
ISBN 10: 0521192242 ISBN 13: 9780521192248
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Descrizione libro Cambridge University Press, 2011. Hardcover. Condizione libro: New. Codice libro della libreria DADAX0521192242

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Descrizione libro Cambridge University Press, 2011. HRD. Condizione libro: New. New Book.Shipped from US within 10 to 14 business days.THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Codice libro della libreria IP-9780521192248

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Descrizione libro Cambridge University Press, 2016. Paperback. Condizione libro: New. PRINT ON DEMAND Book; New; Publication Year 2016; Not Signed; Fast Shipping from the UK. No. book. Codice libro della libreria ria9780521192248_lsuk

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Descrizione libro Cambridge University Press, 2011. HRD. Condizione libro: New. New Book. Delivered from our US warehouse in 10 to 14 business days. THIS BOOK IS PRINTED ON DEMAND.Established seller since 2000. Codice libro della libreria IP-9780521192248

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EDITED BY RON BEKKERMAN , MIKHAIL BILENKO , JOHN LANGFORD
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Descrizione libro 2012. Hardback. Condizione libro: NEW. 9780521192248 This listing is a new book, a title currently in-print which we order directly and immediately from the publisher. Codice libro della libreria HTANDREE0450949

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Descrizione libro Cambridge University Press, 2017. Hardcover. Condizione libro: New. Never used! This item is printed on demand. Codice libro della libreria P110521192242

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Descrizione libro Cambridge University Press, 2017. Hardcover. Condizione libro: New. Never used! This item is printed on demand. Codice libro della libreria 0521192242

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Editore: CAMBRIDGE UNIVERSITY PRESS, United Kingdom (2012)
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Descrizione libro CAMBRIDGE UNIVERSITY PRESS, United Kingdom, 2012. Hardback. Condizione libro: New. Language: English . Brand New Book ***** Print on Demand *****.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. Codice libro della libreria APC9780521192248

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Editore: CAMBRIDGE UNIVERSITY PRESS, United Kingdom (2012)
ISBN 10: 0521192242 ISBN 13: 9780521192248
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Descrizione libro CAMBRIDGE UNIVERSITY PRESS, United Kingdom, 2012. Hardback. Condizione libro: New. Language: English . Brand New Book ***** Print on Demand *****. 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. Codice libro della libreria APC9780521192248

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Bekkerman, Ron [Editor]; Bilenko, Mikhail [Editor]; Langford, John [Editor];
Editore: Cambridge University Press (2011)
ISBN 10: 0521192242 ISBN 13: 9780521192248
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Descrizione libro Cambridge University Press, 2011. Hardcover. Condizione libro: New. Codice libro della libreria INGM9780521192248

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