Sie sind hier: Startseite Seminar Prof. Dr. Ingo Steinwart

Prof. Dr. Ingo Steinwart

— abgelegt unter:

(Localized) learning with kernels

  • FDM-Seminar
Wann 16.06.2017
von 12:00 bis 13:00
Wo Eckerstraße 1, Raum 404, 4. OG
Termin übernehmen vCal

Using reproducing kernel Hilbert spaces in non-parametric approaches
for regression and classification has a long-standing history. In
the first part of this talk, I will introduce these kernel-based learning
(KBL) methods and discuss some existing statistical guarantees for them.
In the second part I will present a localization approach that addresses
the super-linear computational requirements of KBLs in terms of the number
of training samples. I will further provide a statistical analysis that
shows that the "local KBL" achieves the same learning rates as the original,
global KBL. Furthermore, I will report from some large scale experiments
showing that the local KBL achieves essentially the same test performance
as the global KBL, but for a fraction of the computational requirements.
In addition, it turns out that the computational requirements for the local
KBLs are similar to those of a vanilla random chunk approach, while the
achieved test errors are in most cases significantly better. Finally, if time
permits, I will briefly explain, how these methods are being made available
in a recent software package.

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