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Large-scale kernel machines

Auteur : Léon Bottou
Éditeur: Cambridge, Mass. : MIT Press, ©2007.
Collection: Neural information processing series.
Édition/format:   Livre imprimé : AnglaisVoir toutes les éditions et tous les formats
Résumé:
"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."--Jacket.
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Type d’ouvrage: Ressource Internet
Type de document: Livre, Ressource Internet
Tous les auteurs / collaborateurs: Léon Bottou
ISBN: 9780262026253 0262026252
Numéro OCLC: 79002103
Description: xii, 396 pages : illustrations ; 26 cm.
Contenu: Support vector machine solvers / Léon Bottou and Chih-Jen Lin --
Training a support vector machine in the primal / Olivier Chapelle --
Fast kernel learning with sparse inverted index / Patrick Haffner and Stephan Kanthak --
Large-scale learning with string kernels / Soren Sonnenburg, Gunnar Ratsch, and Konrad Rieck --
Large-scale parallel SVM implementation / Igor Durdanovic, Eric Cosatto, and Hans-Peter Graf --
A distributed sequential solver for large-scale SVMs / Elad Yom-Tov --
Newton methods for fast semisupervised linear SVMs / Vikas Sindhwani and S. Sathiya Keerthi --
The improved fast gauss transform with applications to machine learning / Vikas Chandrakant Raykar and Ramani Duraiswami --
Approximation methods for gaussian process regression / Joaquin Quiñonero-Candela, Carl Edward Rasmussen, and Christopher K.I. Williams --
Brisk kernel independent component analysis / Stefanie Jegelka and Arthur Gretton --
Building SVMs with reduced classifier complexity / S. Sathiya Keerthi, Olivier Chapelle, and Dennis DeCost --
Trading convexity for scalability / Ronan Collobert [and others] --
Training invariant SVMs using selective sampling / Gaelle Loosli, Léon Bottou, and Stéphane Canu --
Scaling learning algorithms toward AI / Yoshua Bengio and Yann LeCun.
Titre de collection: Neural information processing series.
Responsabilité: [edited by] Léon Bottou [and others].
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Résumé:

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.  Lire la suite...

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