02443nam a22003618a 4500001001600000003000700016005001700023006001900040007001500059008004100074020002600115020002900141040002400170050002300194082001600217245013100233264005200364300005900416336002600475337002600501338003600527500007300563520112600636650002101762650001601783650002401799650004201823700004401865700004601909700004401955776003501999856004702034CR9781139042918UkCbUP20171017135639.0m|||||o||d||||||||cr||||||||||||110302s2011||||enk     s     ||1 0|eng|d  a9781139042918 (ebook)  z9780521192248 (hardback)  aUkCbUPcUkCbUPerda00aQ325.5 b.S28 201200a006.3/122300aScaling up Machine Learning :bParallel and Distributed Approaches /cEdited by Ron Bekkerman, Mikhail Bilenko, John Langford. 1aCambridge :bCambridge University Press,c2011.  a1 online resource (492 pages) :bdigital, PDF file(s).  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  aTitle from publisher's bibliographic system (viewed on 09 Oct 2015).  aThis 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. 0aMachine learning 0aData mining 0aParallel algorithms 0aParallel programs (Computer programs)1 aBekkerman, Ron,eeditor of compilation.1 aBilenko, Mikhail,eeditor of compilation.1 aLangford, John,eeditor of compilation.08iPrint version: z978052119224840uhttp://dx.doi.org/10.1017/CBO9781139042918