Advances in Large-Margin Classifiers

Bartlett, Peter J. (Editor); Schölkopf, Bernhard (Editor); Schuurmans, Dale (Editor); Smola, Alex J. (Editor)

ISBN 10: 0262194481 / ISBN 13: 9780262194488

Edité par The MIT Press, New York

A propos de cet article

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N° de réf. du vendeur 08937

Détails bibliographiques

• Titre: Advances in Large-Margin Classifiers
• Éditeur: The MIT Press, New York
• Langue: anglais
• ISBN à 10 chiffres: 0262194481
• ISBN à 13 chiffres: 9780262194488
• Reliure: Hardcover
• Édition: First Edition.
• État de l'article: New
• État de la jaquette: New
• Dimensions: 10,3 L x 8,4 H x 1,2 P (en pouces) 10,3 pouces de largeur par 8,4 pouces de hauteur par 1,2 pouces de profondeur
• Catalogues du vendeur:
  • Algorithms & procedures

A propos de ce titre

Synopsis

The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research.

The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification—that is, a scale parameter—rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms. The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.

À propos de l?auteur

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

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.