11. Support Vector Machines
Published Online: 22 AUG 2012
DOI: 10.1002/9781118437957.ch11
Copyright © 2010 John Wiley & Sons, Ltd
Book Title
Machine Learning in Image Steganalysis
Additional Information
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
Publication History
- Published Online: 22 AUG 2012
- Published Print: 21 AUG 2012
ISBN Information
Print ISBN: 9780470663059
Online ISBN: 9781118437957
- Summary
- Chapter
Keywords:
- 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
Summary
This chapter contains sections titled:
-
Linear Classifiers
-
The Kernel Function
-
í-SVM
-
Multi-class Methods
-
One-class Methods
-
Summary