11. Support Vector Machines

  1. Hans Georg Schaathun

Published Online: 22 AUG 2012

DOI: 10.1002/9781118437957.ch11

Machine Learning in Image Steganalysis

Machine Learning in Image Steganalysis

How to Cite

Schaathun, H. G. (2012) Support Vector Machines, in Machine Learning in Image Steganalysis, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9781118437957.ch11

Author Information

  1. Ålesund University College, Norway

Publication History

  1. Published Online: 22 AUG 2012
  2. Published Print: 21 AUG 2012

ISBN Information

Print ISBN: 9780470663059

Online ISBN: 9781118437957



  • Support Vector Machines;
  • SVM, algorithms for starters in machine learning;
  • SVM, to one-class/multi-class as binary to multi-class classifier;
  • SVM as a linear classifier;
  • non-separable, and number of classification errors;
  • minimisation problem from linearly separable, errors to cost function;
  • SVM success, and the so-called kernel trick;
  • simple linear, kernel trick in calculating non-linear, and the linear;
  • kernel trick, generalising to other algorithms;
  • multi-class, one-against-one with faster training time


This chapter contains sections titled:

  • Linear Classifiers

  • The Kernel Function

  • í-SVM

  • Multi-class Methods

  • One-class Methods

  • Summary