Scaling Up Machine Learning Parallel and Distributed Approaches

Bekkerman, Ron & Mikhail Bilenko & John Langford

ISBN 10: 0521192242 / ISBN 13: 9780521192248

Edité par Cambridge University Press, 2011

A propos de cet article

Light rubbing and toning overall and some light scratches. Interior pages clean and unmarked. A tight and clean copy. Photos upon request. International shipping billed at cost.; 4to 11" - 13" tall; 492 pages. N° de réf. du vendeur 89027

Détails bibliographiques

Titre : Scaling Up Machine Learning Parallel and ...

Éditeur : Cambridge University Press

Date d'édition : 2011

Reliure : Hardcover

Etat : Very Good+

Etat de la jaquette : No Dust Jacket

Edition : First Edition.

A propos de ce titre

Revue de presse:

'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