New for: D3
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.