bilenko mikhail (35 resultados)

Hardcover. Condición: Very Good+. Estado de la sobrecubierta: 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.

Condición: 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.

hardcover. Condición: 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!

Condición: New.

hardcover. Condición: New. BRAND NEW! Ships within 24 hours!

Condición: As New. Unread book in perfect condition.

Condición: New. In.

Paperback. Condición: New.

Condición: New.

Condición: New. 2018. Reprint. Paperback. . . . . .

Condición: As New. Unread book in perfect condition.

Condición: New. Reprint edition rsity Press UK NO-PA16APR2015-KAP.

Condición: New. 2018. Reprint. Paperback. . . . . . Books ship from the US and Ireland.

Paperback. Condición: Brand New. 1st reprint edition. 492 pages. 9.88x7.01x1.50 inches. In Stock.

hardcover. Condición: new.

Hardcover. Condición: 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.

Condición: New. In.

Condición: New. This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies. Editor(s): Bekkerman, Ron; Bilenko, Mikhail; Langford, John. Num Pages: 492 pages, 144 b/w illus. BIC Classification: UYQM; UYQP. Category: (U) Tertiary Education (US: College). Dimension: 256 x 185 x 31. Weight in Grams: 1078. . 2012. Illustrated. hardcover. . . . .

Condición: New. This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies. Editor(s): Bekkerman, Ron; Bilenko, Mikhail; Langford, John. Num Pages: 492 pages, 144 b/w illus. BIC Classification: UYQM; UYQP. Category: (U) Tertiary Education (US: College). Dimension: 256 x 185 x 31. Weight in Grams: 1078. . 2012. Illustrated. hardcover. . . . . Books ship from the US and Ireland.

Hardcover. Condición: Brand New. 488 pages. 10.00x7.20x1.30 inches. In Stock.

Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Hardcover. Condición: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

paperback. Condición: New. Paperback. Pub Date: 2021-03-01 Pages: 496 Language: Chinese Publisher: National Defense Industry Press Large-scale Machine Learning: Parallel and Distributed Technology The content involves the parallelization of some machine learning algorithms. making large-scale distributed machines Learning algorithms become possible. The content is divided into four parts: large-scale machine learning frameworks. supervised and unsupervised learning algorithms. other learning algorithms and related appl.

Paperback. Condición: new. Paperback. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condición: Brand New. 1st reprint edition. 492 pages. 9.88x7.01x1.50 inches. In Stock. This item is printed on demand.

Paperback / softback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Condición: New. PRINT ON DEMAND.

Paperback. Condición: new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 vari.

Paperback. Condición: new. Paperback. 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.