Yet another classification method used in machine learning. Here is the most accessible tutorial I found on this topic, by Tristan Fletcher at UCL. It might also be useful to see how you can use SVMs for regression (to predict continuous variables, instead of classes). This technical report, by Steve Gunn at U of Southampton, was the one that added the most intuition among the tutorials I found on SVMs, along with Geoff Hinton’s notes. And if you are interested in having a library that implements different SVMs then you might want to take a look at Shogun. It provides interfaces to Matlab, R, Octave and Python. It seems like a pretty neat library — at least from what I can see on its website.