Reproducing kernel Hilbert Spaces (RKHS) play an important role in several branches of mathematics including statistical learning theory and stochastic processes. In this talk we will investigate approximation properties of RKHS with the help of certain interpolation spaces. Here the main tool is a generalized version of the classical Mercer theorem, which makes it possible to describe these interpolation spaces by weighted sequence spaces.

In the second part we illustrate how the general theory leads to generalized Karhunen-Loeve expansions of stochastic processes. In particular, we discuss their path behavior and our ability to approximate their paths in strong norms.

Referent: Professor Dr. Ingo Steinwart, Universität Stuttgart

Die Vorträge finden jeweils um 17.15 Uhr im Raum 210 des Mathematik-Gebäudes 48 statt. Zuvor gibt es ab 16.45 Uhr die Gelegenheit, den Sprecher beim Kolloquiumstee in Raum 580 zu treffen.