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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Steinwart, Ingo (Autor), Christmann, Andreas (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Springer New York : Imprint: Springer, 2008.
Edición:1st ed. 2008.
Colección:Information Science and Statistics,
Temas:
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 .
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 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.

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