Support Vector Machines for Pattern Classification

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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Bibliographic Details
Main Author: Abe, Shigeo. (Author)
Corporate Author: SpringerLink (Online service)
Format: Electronic
Language:English
Published: London : Springer London, 2010.
Series:Advances in Pattern Recognition,
Subjects:
Online Access:https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-1-84996-098-4
Table of Contents:
  • Introduction
  • Two-Class Support Vector Machines
  • Multiclass Support Vector Machines
  • Variants of Support Vector Machines
  • Training Methods
  • Kernel-Based Methods
  • Feature Selection and Extraction
  • Clustering
  • Maximum-Margin Multilayer Neural Networks
  • Maximum-Margin Fuzzy Classifiers
  • Function Approximation.