• 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 maximum-margin hyper-plane
depends on the square of the number of training cases.
– We need to store all the support vectors.
• SVM’s 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
higher-dimensional space, thus giving a non-linear version
of PCA in the original space.