














• 
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).

