ICML 2007 - The 24th Annual International Conference on Machine Learning

Kernel Tricks, Means and Ends

author: Bernhard Schölkopf, Max Planck Institute for Biological Cybernetics

Description

I will present my thoughts on what made kernel machines popular and what may or may not keep them going. I will also discuss applications in different domains, including computer graphics.

Categories

Top: Computer Science: Machine Learning: Kernel Methods

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*Slides*
0:00	Kernel means
0:00	Kernel means
17:29	An example of a kernel algorithm revisited
20:37	The mean map-01
23:54	The mean map-02
24:46	The mean map for measures
27:56	Uniform convergence bounds
32:29	Application 1: Two-sample problem-01
34:35	Application 1: Two-sample problem-02
36:13	Application 2: dependence measures-01
38:00	Application 2: dependence measures-02
38:44	Application 3: Covariate shift correction and local learning
40:59	Application 4: measure estimation and dataset squashing
42:09	References-01
43:01	References-02
43:13	References-03
43:23	References-04

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