Support Vector Machines

This book explains the principles that make support vector machines (SVMs) a successful modelling and prediction tool for a variety of applications. The authors present the basic ideas of SVMs together with the latest developments and current research questions in a unified style. They identify thre...

Description complète

Détails bibliographiques
Cote:Libro Electrónico
Auteurs principaux: Steinwart, Ingo (Auteur), Christmann, Andreas (Auteur)
Collectivité auteur: SpringerLink (Online service)
Format: Électronique eBook
Langue:Inglés
Publié: New York, NY : Springer New York : Imprint: Springer, 2008.
Édition:1st ed. 2008.
Collection:Information Science and Statistics,
Sujets:
Artificial intelligence.
Computer science-Mathematics.
Mathematical statistics.
Pattern recognition systems.
Data mining.
Signal processing.
Artificial Intelligence.
Probability and Statistics in Computer Science.
Automated Pattern Recognition.
Data Mining and Knowledge Discovery.
Signal, Speech and Image Processing .
Accès en ligne:Texto Completo
Table des matières:
  • Loss Functions and Their Risks
  • Surrogate Loss Functions (*)
  • Kernels and Reproducing Kernel Hilbert Spaces
  • Infinite-Sample Versions of Support VectorMachines
  • Basic Statistical Analysis of SVMs
  • Advanced Statistical Analysis of SVMs (*)
  • Support Vector Machines for Classification
  • Support Vector Machines for Regression.
  • Robustness
  • Computational Aspects
  • Data Mining.

Documents similaires