| Record ID | marc_loc_updates/v39.i44.records.utf8:7202231:1603 |
| Source | Library of Congress |
| Download Link | /show-records/marc_loc_updates/v39.i44.records.utf8:7202231:1603?format=raw |
LEADER: 01603cam a22003258a 4500
001 2011016323
003 DLC
005 20111026171718.0
008 110425s2011 enk 001 0 eng
010 $a 2011016323
020 $a9780521192248 (hardback)
040 $aDLC$cDLC
042 $apcc
050 00 $aQ325.5$b.S28 2011
082 00 $a006.3/1$223
084 $aCOM016000$2bisacsh
245 00 $aScaling up machine learning :$bparallel and distributed approaches /$c[edited by] Ron Bekkerman, Mikhail Bilenko, John Langford.
260 $aCambridge ;$aNew York :$bCambridge University Press,$c2011.
263 $a1110
300 $ap. cm.
500 $aIncludes index.
520 $a"This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs, and constraints of the available options"--$cProvided by publisher.
650 0 $aMachine learning.
650 0 $aData mining.
650 0 $aParallel algorithms.
650 0 $aParallel programs (Computer programs)
650 7 $aCOMPUTERS / Computer Vision & Pattern Recognition.$2bisacsh
700 1 $aBekkerman, Ron.
700 1 $aBilenko, Mikhail,$d1978-
700 1 $aLangford, John,$d1975-