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This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.
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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.
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.
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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Scaling up Machine Learning: Parallel and Distributed Approaches
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Cambridge University Press, 2011
ISBN 10: 0521192242
ISBN 13: 9780521192248
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hardcover. Condizione: 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! Codice articolo S_399948650
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Scaling Up Machine Learning: Parallel and Distributed Approaches
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Cambridge University Press, 2011
ISBN 10: 0521192242
ISBN 13: 9780521192248
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Hardcover. Condizione: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less 2.2. Codice articolo G0521192242I4N00
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Scaling up Machine Learning : Parallel and Distributed Approaches
Editore:
Cambridge University Press, 2011
ISBN 10: 0521192242
ISBN 13: 9780521192248
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Condizione: Good. Used book that is in clean, average condition without any missing pages. Codice articolo 9388732-75
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Scaling Up Machine Learning Parallel and Distributed Approaches
Editore:
Cambridge University Press, 2011
ISBN 10: 0521192242
ISBN 13: 9780521192248
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Hardcover. Condizione: Very Good+. Condizione sovraccoperta: 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. Codice articolo 89027
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Scaling Up Machine Learning: Parallel and Distributed Approaches
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Cambridge University Press, 2012
ISBN 10: 0521192242
ISBN 13: 9780521192248
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Condizione: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1100grams, ISBN:9780521192248. Codice articolo 3714955
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Scaling up Machine Learning: Parallel and Distributed Approaches
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Condizione: new. Codice articolo Shelfdream0521192242
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Scaling up Machine Learning: Parallel and Distributed Approaches
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Condizione: New. Codice articolo ABLIING23Feb2215580247123
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Scaling up Machine Learning: Parallel and Distributed Approaches
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Scaling up Machine Learning: Parallel and Distributed Approaches
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Scaling up Machine Learning (Hardcover)
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Cambridge University Press, Cambridge, 2011
ISBN 10: 0521192242
ISBN 13: 9780521192248
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Hardcover. Condizione: 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. Codice articolo 9780521192248
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