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SVM's are awesome for pattern matching. I first encountered them on a project to identify pedestrians from IR images and was blown away with the simplicity of underlaying math.

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For anyone curious, it basically boils down to roughly "put your data in a plane and plot a line through them that has the largest possible margin from each cluster".

With a bunch of "transform it over some other dimension to achieve a good divide between the clusters"

Except that the straight line is in feature space and not input space; the computations are done using only a kernel function, which takes vectors in the input space, and computes the dot product in the feature space.

This is a very important distinction because while the method is linear in the feature space, it can solve non-linear problems in the input space.

It is, but this addendum provides diminishing returns in terms of usefulness versus sentence length/parsability...

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