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
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).