02474nam a22003738a 4500 CR9781139042918 UkCbUP 20171017135639.0 m|||||o||d|||||||| cr|||||||||||| 110302s2011||||enk s ||1 0|eng|d 9781139042918 (ebook) 9780521192248 (hardback) UkCbUP UkCbUP rda Q325.5 .S28 2012 006.3/1 23 Scaling up Machine Learning : Parallel and Distributed Approaches / Edited by Ron Bekkerman, Mikhail Bilenko, John Langford. Cambridge : Cambridge University Press, 2011. 1 online resource (492 pages) : digital, PDF file(s). text txt rdacontent computer c rdamedia online resource cr rdacarrier Title from publisher's bibliographic system (viewed on 09 Oct 2015). 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. Machine learning Data mining Parallel algorithms Parallel programs (Computer programs) Bekkerman, Ron, editor of compilation. Bilenko, Mikhail, editor of compilation. Langford, John, editor of compilation. Print version: 9780521192248 http://dx.doi.org/10.1017/CBO9781139042918 221660 221660