Support vector machines in high-energy physics

This lecture introduces the support vector algorithms for classi cation and regression. They are an application of the so-called kernel trick, which allows the extension of a certain class of linear algorithms to the non-linear case. The kernel trick will be introduced and in the context of structural risk minimization, large margin algorithms for classi cation and regression will be presented. Current applications in high-energy physics will be discussed.