Prof. Dr. Ingo Steinwart

— abgelegt unter:

(Localized) Learning with Kernels

  • FDM Seminar
Wann 16.06.2017
von 12:00 bis 13:00
Wo Room 404, Eckerstr. 1
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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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