Welcome to Central Library, SUST

Scaling up Machine Learning : (Record no. 38813)

MARC details
000 -LEADER
fixed length control field 02438nam a22003617a 4500
001 - CONTROL NUMBER
control field sulb-eb0017375
003 - CONTROL NUMBER IDENTIFIER
control field BD-SySUS
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20160405140651.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 110302s2011||||enk o ||1 0|eng|d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781139042918 (ebook)
Canceled/invalid ISBN 9780521192248 (hardback)
040 ## - CATALOGING SOURCE
Original cataloging agency UkCbUP
Language of cataloging eng
Description conventions rda
Transcribing agency UkCbUP
Modifying agency BD-SySUS.
050 00 - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q325.5
Item number .S28 2012
082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3/1
Edition number 23
245 00 - TITLE STATEMENT
Title Scaling up Machine Learning :
Remainder of title Parallel and Distributed Approaches /
Statement of responsibility, etc. edited by Ron Bekkerman, Mikhail Bilenko, John Langford.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Cambridge :
Name of producer, publisher, distributor, manufacturer Cambridge University Press,
Date of production, publication, distribution, manufacture, or copyright notice 2011.
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource (492 pages) :
Other physical details digital, PDF file(s).
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type term computer
Media type code c
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term online resource
Carrier type code cr
Source rdacarrier
500 ## - GENERAL NOTE
General note Title from publisher's bibliographic system (viewed on 04 Apr 2016).
520 ## - SUMMARY, ETC.
Summary, etc. 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.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning
Topical term or geographic name as entry element Data mining
Topical term or geographic name as entry element Parallel algorithms
Topical term or geographic name as entry element Parallel programs (Computer programs)
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Bekkerman, Ron,
Relator term editor.
Personal name Bilenko, Mikhail,
Relator term editor.
Personal name Langford, John,
Relator term editor.
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Print version:
International Standard Book Number 9780521192248
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="http://dx.doi.org/10.1017/CBO9781139042918">http://dx.doi.org/10.1017/CBO9781139042918</a>
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Koha item type

No items available.