Support Vector Machines
A
support vector machine (SVM) is a statistical supervised learning
technique from the field of machine learning applicable to both classification
and regression. The original SVM algorithm was invented by Vladimir N. Vapnik
and the current standard incarnation (soft margin) was proposed by Corinna
Cortes and Vapnik in 1993 and published 1995. [1] SVMs use the spirit of the Structural
Risk Minimization principle.
A
support vector machine constructs a hyperplane or set of hyperplanes in a high
or infinite dimensional space, which can be used for classification,
regression, or other tasks. Good separation is achieved by the hyperplane that
has the largest distance to the nearest training data point of any class
(so-called functional margin), since in general the larger the margin the lower
the generalization error of the classifier. SVMs are used to classify linear
and nonlinear separation models.
Whereas
the original problem may be stated in a finite dimensional space, it often
happens that the sets to discriminate are not linearly separable in that space.
For this reason, it was proposed that the original finite-dimensional space be
mapped into a much higher-dimensional space, presumably making the separation
easier in that space. To keep the computational load reasonable, the mappings
used by SVM schemes are designed to ensure that dot products may be computed
easily in terms of the variables in the original space, by defining them in
terms of a kernel function K(x,y) selected to suit the problem.
