Advances in kernel methods : support vector learning /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Schölkopf, Bernhard, Burges, Christopher J. C., Smola, Alexander J.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge, Mass. : MIT Press, ©1999.
©1999
Temas:
Machine learning.
Algorithms.
Kernel functions.
Algorithms
Machine Learning
Apprentissage automatique.
Algorithmes.
Noyaux (Mathématiques)
algorithms.
COMPUTERS > Enterprise Applications > Business Intelligence Tools.
COMPUTERS > Intelligence (AI) & Semantics.
Kernel functions
Machine learning
Kunstmatige intelligentie.
Algoritmen.
Patroonherkenning.
Functies (wiskunde)
Machine-learning.
Congressen (vorm)
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Preface
  • Introduction to support vector learning
  • Roadmap
  • Three remarks on the support vector method of function estimation / Valdimir Vapnik
  • Generalization performance of support vector machines and other pattern classifiers / Peter Bartlett and John Shawe-Taylor
  • Bayesian voting schemes and large margin classifiers / Nello Cristianini and John Shawe-Taylor
  • Support vector machines, reproducing kernal Hilbert spaces, and randomized GACV / Grace Wahba
  • Geometry and invariance in kernel based methods / Christopher J.C. Burgess
  • On the annealed VC entropy for margin classifiers : a statistical mechanics study / Manfred Opper
  • Entropy numbers, operators and support vector kernels / Robert C. Williamson, Alex J. Smola and Berhard Schölkopf
  • Solving the quadratic programming problem arising in support vector classification / Linda Kaufman
  • Making large-scale support vector machine learning practical / Thorsten Joachims
  • Fast training of support vector machines using sequential minimal optimization / John C. Platt
  • Support vector machines for dynamic reconstruction of a chaotic system / David Mattera and Simon Haykin
  • Using support vector machines for time series prediction / Klaus-Robert Müller . [and others]
  • Pairwise classification and support vector machines / Ulrich Kressel
  • Reducing the run-time complexity in support vector machines / Edgar E. Osuna and Federico Girosi
  • Support vector regression with ANOVA decomposition kernels / Mark O. Stitson . [and others]
  • Support vector density estimation / Jason Weston . [and others]
  • Combining support vector and mathematical programming methods for classification / Kristin P. Bennett
  • Kernel principal component analysis / Berhard Schölkopf, Alex J. Smola and Klaus-Robert Müller.

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