Scaling up Machine Learning : Parallel and Distributed Approaches - Ron Bekkerman

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Scaling up Machine Learning

Parallel and Distributed Approaches

By: Ron Bekkerman (Editor), Mikhail Bilenko (Editor), John Langford (Editor)

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Hardcover | 30 December 2011

At a Glance

Hardcover
492 Pages

Dimensions(cm)
25.4 x 17.78 x 2.69

Hardcover

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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.

Industry Reviews

'One of the landmark achievements of our time is the ability to extract value from large volumes of data. Engineering and algorithmic developments on this front have gelled substantially in recent years, and are quickly being reduced to practice in widely available, reusable forms. This book provides a broad and timely snapshot of the state of developments in scalable machine learning, which should be of interest to anyone who wishes to understand and extend the state of the art in analyzing data.' Joseph M. Hellerstein, University of California, Berkeley
'This is a book that every machine learning practitioner should keep in their library.' Yoram Singer, Google Inc.
'The contributions in this book run the gamut from frameworks for large-scale learning to parallel algorithms to applications, and contributors include many of the top people in this burgeoning subfield. Overall this book is an invaluable resource for anyone interested in the problem of learning from and working with big datasets.' William W. Cohen, Carnegie Mellon University, Pennsylvania
'This unique, timely book provides a 360 degrees view and understanding of both conceptual and practical issues that arise when implementing leading machine learning algorithms on a wide range of parallel and high-performance computing platforms. It will serve as an indispensable handbook for the practitioner of large-scale data analytics and a guide to dealing with BIG data and making sound choices for efficient applying learning algorithms to them. It can also serve as the basis for an attractive graduate course on parallel/distributed machine learning and data mining.' Joydeep Ghosh, University of Texas

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Computer Vision Computer Programming & Software Development Algorithms & Data Structures Machine Learning Pattern Recognition

Scaling up machine learning: introduction
Frameworks for Scaling Up Machine Learning:
Mapreduce and its application to massively parallel learning of decision tree ensembles
Large-scale machine learning using DryadLINQ
IBM parallel machine learning toolbox
Uniformly fine-grained data parallel computing for machine learning algorithms
Supervised and Unsupervised Learning Algorithms:
PSVM: parallel support vector machines with incomplete Cholesky Factorization
Massive SVM parallelization using hardware accelerators
Large-scale learning to rank using boosted decision trees Krysta
The transform regression algorithm
Parallel belief propagation in factor graphs
Distributed Gibbs sampling for latent variable models Arthur Asuncion
Large-scale spectral clustering with Mapreduce and MPI
Parallelizing information-theoretic clustering methods
Alternative Learning Settings:
Parallel online learning
Parallel graph-based semi-supervised learning
Distributed transfer learning via cooperative matrix factorization
Parallel large-scale feature selection
Applications:
Large-scale learning for vision with GPUS
Large-scale FPGA-based convolutional networks Clement Farabet
Mining tree structured data on multicore systems
Scalable parallelization of automatic speech recognition
Table of Contents provided by Publisher. All Rights Reserved.

Scaling up Machine Learning

Parallel and Distributed Approaches

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

You Can Find This Book In

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