Kernel-based techniques (such as support vector machines,
Bayes point machines, kernel principal component analysis,
and Gaussian processes) represent a major development in
machine learning algorithms.
Support vector machines (SVM) are a group of supervised
learning methods that can be applied to classification or
regression. Support vector machines represent an extension
to nonlinear models of the generalized portrait algorithm developed by
The SVM algorithm is based on the statistical learning
theory and the Vapnik-Chervonenkis (VC) dimension introduced by
and Alexey Chervonenkis.