Learning and inference with positive definite kernels

Schölkopf, B

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Learning and inference with positive definite kernels

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Schölkopf, B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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http://sanum.github.io/files/sanum2010.pdf
(Abstract)

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Citation

Schölkopf, B. (2010). Learning and inference with positive definite kernels. Talk presented at 34th Annual South African Symposium on Numerical and Applied Mathematics (SANUM 2010). Stellenbosch, South Africa. 2010-03-15 - 2010-03-17.

Cite as: https://hdl.handle.net/21.11116/0000-0002-B155-6

Abstract

Kernel methods have become one of the most widely used techniques in the field of machine learning. I will present my thoughts on what made them popular and where things are heading. I will discuss some recent developments for two-sample and independence testing as well as applications in different domains.