- Hardcover: 492 pages
- Publisher: Cambridge University Press; 1 edition (December 30, 2011)
- Language: English
- ISBN-10: 0521192242
- ISBN-13: 978-0521192248
- Product Dimensions: 8.5 x 1.3 x 10 inches
- Shipping Weight: 2.2 pounds (View shipping rates and policies)
- Average Customer Review: 2 customer reviews
- Amazon Best Sellers Rank: #1,938,414 in Books (See Top 100 in Books)
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Scaling up Machine Learning: Parallel and Distributed Approaches 1st Edition
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"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.
"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
"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
"... an excellent resource for researchers in the field."
J. Arul for Computing Reviews
In many practical situations, it is impossible to run existing machine learning methods on a single computer, because either the data is too large, or the speed and throughput requirements are too demanding. Researchers and practitioners will find here a variety of machine learning methods developed specifically for parallel or distributed systems, covering algorithms, platforms and applications.