Scaling up Machine Learning

出版者:Cambridge University Press

作者:Bekkerman, Ron; Bilenko, Mikhail; Langford, John

页数:492

出版时间:2011-12-30

价格:USD 90.00

装帧:Hardcover

isbn号码:9780521192248

图书标签:机器学习数据挖掘分布式并行计算机MachineLearning计算机科学集体智慧

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.

用户评价

asdasda

虽然一开始说是面向工程师的，但还是有点过于偏向理论了，而且是由一篇篇独立论文组成的，深浅不一，内容感觉太杂，什么领域的都有

asdasda

不是我要的distributed learning.

虽然一开始说是面向工程师的，但还是有点过于偏向理论了，而且是由一篇篇独立论文组成的，深浅不一，内容感觉太杂，什么领域的都有