Support Vector Machines are Perceptrons!
		• SVM’s use each training case, x, to define a feature K(x, .) where K is chosen by the user. – So the user designs the features. • Then they do “feature selection” by picking the support vectors, and they learn how to weight the features by solving a big optimization problem. • So an SVM is just a very clever way to train a standard perceptron. – All of the things that a perceptron cannot do cannot be done by SVM’s (but it’s a long time since 1969 so people have forgotten this).