MIT Press
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, 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-TovLéon Bottou is a Research Scientist at NEC
Labs America. Olivier Chapelle is with Yahoo! Research. He is editor of
Semi-Supervised Learning (MIT Press, 2006). Dennis DeCoste is with Microsoft
Research. Jason Weston is a Research Scientist at NEC Labs America.
Bottou / Chapelle / DeCoste
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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, 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-TovLéon Bottou is a Research Scientist at NEC
Labs America. Olivier Chapelle is with Yahoo! Research. He is editor of
Semi-Supervised Learning (MIT Press, 2006). Dennis DeCoste is with Microsoft
Research. Jason Weston is a Research Scientist at NEC Labs America.
Autoren/Hrsg.
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