SVMs are lickety-split simple. You're drawing the best line/plane/hyperplane between some points. You can turn this into a nice convex optimization program, giv

SVMs are lickety-split simple. You're drawing the best line/plane/hyperplane between some points. You can turn this into a nice convex optimization program, given some conditions. If this thing isn't fully separable, you can fudge it a little with some penalty terms, or you can cheaply project these points into some space where such a separation does exist. The hard part will always be your feature extraction, labeled data collection, and the parameter tuning for everything I just waved my hand at.