
















Support Vector
Machines work very well in practice.





The
user must choose the kernel function and its



parameters,
but the rest is automatic.





The
test performance is very good.




They can be
expensive in time and space for big datasets





The
computation of the maximummargin hyperplane



depends
on the square of the number of training cases.




We
need to store all the support vectors.




SVMs are very
good if you have no idea about what



structure to
impose on the task.




The kernel trick
can also be used to do PCA in a much



higherdimensional
space, thus giving a nonlinear version


of PCA in the
original space.

