Support Vector Learning

solving Pattern Recognition and Regression Estimation problems. The
method is based on results in the statistical theory of learning with
finite sample sizes developed by Vapnik and co-workers. Crucial to SV
learning are two ideas: automatic capacity (or complexity) control of
the learnt functions, and nonlinear maps into feature spaces given via
kernels.
In the talk, I will try to explain these two basic ideas and show how
they are used to come up with a learning algorithm.