An Idiot’s guide to Support vector machines (2003) [pdf]
I&#x27;ve been through SVM explanations before and I always get to the part where they start talking about kernels and the logic seems recursive. ie: if your data isn&#x27;t linearly separable, let&#x27;s choose a kernel that matches the shape of the data, and like magic it&#x27;s now separable.  But guess what, the reason I&#x27;m doing machine learning is I don&#x27;t know the shape of my data. If I knew that, and if there was actually a simple transformation, I would just transform it in the first place and use a linear model. So it seems like a bit of a bait and switch. Is there some part of this that can automatically select a transformation in a similar manner to tuning hyper parameters in a neural net?<p>Would love it if anybody would explain this to me :-)