Advances in Kernel Methods: Support Vector Learning - Couverture rigide

Couverture rigide

ISBN 10 : 0262194163 ISBN 13 : 9780262194167

Editeur : The MIT Press, 1998

Synopsis

The Support Vector Machine is a powerful new learning algorithm for solving a variety of learning and function estimation problems, such as pattern recognition, regression estimation, and operator inversion. The impetus for this collection was a workshop on Support Vector Machines held at the 1997 NIPS conference. The contributors, both university researchers and engineers developing applications for the corporate world, form a Who's Who of this exciting new area.

Contributors
Peter Bartlett, Kristin P. Bennett, Christopher J.C. Burges, Nello Cristianini, Alex Gammerman, Federico Girosi, Simon Haykin, Thorsten Joachims, Linda Kaufman, Jens Kohlmorgen, Ulrich Kreßel, Davide Mattera, Klaus-Robert Müller, Manfred Opper, Edgar E. Osuna, John C. Platt, Gunnar Rätsch, Bernhard Schölkopf, John Shawe-Taylor, Alexander J. Smola, Mark O. Stitson, Vladimir Vapnik, Volodya Vovk, Grace Wahba, Chris Watkins, Jason Weston, Robert C. Williamson

A propos du livre

À propos de l?auteur

Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. He is coauthor of Learning with Kernels (2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by the MIT Press.

Alexander J. Smola is Senior Principal Researcher and Machine Learning Program Leader at National ICT Australia/Australian National University, Canberra.

Détails de l'édition

• Éditeur: The MIT Press
• Date d'édition: 1998
• Langue: anglais
• ISBN 10: 0262194163
• ISBN 13: 9780262194167
• Reliure: Relié
• Nombre de pages: 386
• Éditeur: Burges Christopher J.C., Scholkopf Bernhard, Smola Alexander J.
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible