Large-Scale Kernel Machines (Neural Information Processing) - Hardcover

Hardcover

ISBN 10: 0262026252 ISBN 13: 9780262026253

Publisher: MIT Press, 2007

Synopsis

Solutions for learning from large scale datasets, including kernel learning algorithms that scale linearly with the volume of the data and experiments carried out on realistically large datasets.

Pervasive and networked computers have dramatically reduced the cost of collecting and distributing large datasets. In this context, machine learning algorithms that scale poorly could simply become irrelevant. We need learning algorithms that scale linearly with the volume of the data while maintaining enough statistical efficiency to outperform algorithms that simply process a random subset of the data. This volume offers researchers and engineers practical solutions for learning from large scale datasets, with detailed descriptions of algorithms and experiments carried out on realistically large datasets. At the same time it offers researchers information that can address the relative lack of theoretical grounding for many useful algorithms. After a detailed description of state-of-the-art support vector machine technology, an introduction of the essential concepts discussed in the volume, and a comparison of primal and dual optimization techniques, the book progresses from well-understood techniques to more novel and controversial approaches. Many contributors have made their code and data available online for further experimentation. Topics covered include fast implementations of known algorithms, approximations that are amenable to theoretical guarantees, and algorithms that perform well in practice but are difficult to analyze theoretically.

Contributors
Léon Bottou, Yoshua Bengio, Stéphane Canu, Eric Cosatto, Olivier Chapelle, Ronan Collobert, Dennis DeCoste, Ramani Duraiswami, Igor Durdanovic, Hans-Peter Graf, Arthur Gretton, Patrick Haffner, Stefanie Jegelka, Stephan Kanthak, S. Sathiya Keerthi, Yann LeCun, Chih-Jen Lin, Gaëlle Loosli, Joaquin Quiñonero-Candela, Carl Edward Rasmussen, Gunnar Rätsch, Vikas Chandrakant Raykar, Konrad Rieck, Vikas Sindhwani, Fabian Sinz, Sören Sonnenburg, Jason Weston, Christopher K. I. Williams, Elad Yom-Tov

About this title

About the Authors

Lé on Bottou is a Research Scientist at NEC Labs America.

Olivier Chapelle is Senior Research Scientist in Machine Learning at Yahoo.

Dennis DeCoste is with Microsoft Research.

Jason Weston is a Research Scientist at NEC Labs America.

Peter Graf is Professor of Psychology at the University of British Columbia.

Edition details

• Publisher: MIT Press
• Publication date: 2007
• Language: English
• ISBN 10: 0262026252
• ISBN 13: 9780262026253
• Binding: Hardcover
• Number of pages: 396
• Editor: Bottou Research Scientist Léon, Chapelle Olivier, DeCoste Dennis, Weston Jason