Published by The MIT Press, 2007
ISBN 10: 0262026252 ISBN 13: 9780262026253
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Hardcover. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!.
Published by MIT Press, Cambridge, MA, 2007
ISBN 10: 0262026252 ISBN 13: 9780262026253
Cloth. Condition: Very Good to Near Fine. 396 pp. Tightly bound. Corners not bumped. Text is free of markings. The letter "T" stamp on bottom fore-edge.
Published by MIT Press, 2007
ISBN 10: 0262026252 ISBN 13: 9780262026253
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Published by MIT Press, 2007
ISBN 10: 0262026252 ISBN 13: 9780262026253
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Hardcover. Condition: used. Illustrated. 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.ContributorsLéon 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, Galle Loosli, Joaquin Quionero-Candela, Carl Edward Rasmussen, Gunnar Rtsch, Vikas Chandrakant Raykar, Konrad Rieck, Vikas Sindhwani, Fabian Sinz, Sren Sonnenburg, Jason Weston, Christopher K. I. Williams, Elad Yom-Tov 3130.
Published by MIT Press, 2007
ISBN 10: 0262026252 ISBN 13: 9780262026253
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Condition: New. Book is in NEW condition. 3.13.
Published by MIT Press, 2007
ISBN 10: 0262026252 ISBN 13: 9780262026253
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Condition: New. New! This book is in the same immaculate condition as when it was published 3.13.
Published by MIT Press, 2007
ISBN 10: 0262026252 ISBN 13: 9780262026253
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Published by The MIT Press, 2007
ISBN 10: 0262026252 ISBN 13: 9780262026253
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Hardcover. Condition: Good. 0262026252.