Support Vector Machines for Pattern Classification

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Originally formulated for two-class classification problems, support vector machines (SVMs) are now accepted as powerful tools for developing pattern classification and function approximation systems. Recent developments in kernel-based methods include kernel classifiers and regressors and their var...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Abe, Shigeo (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Springer London : Imprint: Springer, 2010.
Edición:2nd ed. 2010.
Colección:Advances in Computer Vision and Pattern Recognition,
Temas:
Control engineering.
Pattern recognition systems.
Natural language processing (Computer science).
Artificial intelligence.
Robotics.
Automation.
Control and Systems Theory.
Automated Pattern Recognition.
Natural Language Processing (NLP).
Artificial Intelligence.
Control, Robotics, Automation.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • Two-Class Support Vector Machines
  • Multiclass Support Vector Machines
  • Variants of Support Vector Machines
  • Training Methods
  • Kernel-Based Methods Kernel@Kernel-based method
  • Feature Selection and Extraction
  • Clustering
  • Maximum-Margin Multilayer Neural Networks
  • Maximum-Margin Fuzzy Classifiers
  • Function Approximation.

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