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  Large-Scale Kernel Machines

Bottou, L., Chapelle, O., DeCoste, D., & Weston, J. (2007). Large-Scale Kernel Machines. Cambridge, MA, USA: MIT Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-CBAB-B Version Permalink: http://hdl.handle.net/21.11116/0000-0003-E8F0-8
Genre: Book

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 Creators:
Bottou, L, Author
Chapelle, O1, 2, Author              
DeCoste, D, Author
Weston, J, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: 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.

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 Dates: 2007-08
 Publication Status: Published in print
 Pages: 396
 Publishing info: Cambridge, MA, USA : MIT Press
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 5370
ISBN: 978-0-262-02625-3
 Degree: -

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Title: Neural Information Processing Series
Source Genre: Series
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Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: -