Coruscation

An SVM corresponds to a linear method in a very high dimensional feature
space which is nonlinearly related to the input space. It does not
involve any computations in that high dimensional space. By the use of
kernels, all necessary computations are performed directly in input space.

are a method for creating functions from a set of labeled training
data. The function can be a classification function (the output is
binary: is the input in a category) or the function can be a general
regression function.

For classification, SVMs operate by finding a hypersurface in the
space of possible inputs. This hypersurface will attempt to split the
positive examples from the negative examples. The split will be chosen
to have the largest distance from the hypersurface to the nearest of
the positive and negative examples. Intuitively, this makes the
classification correct for testing data that is near, but not
identical to the training data.

r (with module e1071):
estimate, predict, example, example2.

Matlab:
Kernel Methods for Pattern Analysis
John Shawe-Taylor & Nello Cristianini
Cambridge University Press, 2004
Detailed contents, inventory of algorithms and kernels, and matlab code.

Stand-alone:

SVM Light is a Support Vector Machine.