Machine Learning Summer School 2004 - Berder Island

Introduction to Kernel Methods

author: Bernhard Schölkopf, Max Planck Institute for Biological Cybernetics

Description

The course will cover the basics of Support Vector Machines and related kerne methods: 1. Kernels and Feature Spaces
2. Large Margin Classification
3. Basic Ideas of Learning Theory
4. Support Vector Machines
5. Examples of Other Kernel Algorithms

Categories

Top: Computer Science: Machine Learning: Kernel Methods

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*Slides*
1:36	Introduction to Kernel Methods
8:34	Learning and Similary:Some Informal Thoughts
11:19	Simlary of Inputs
13:34	An Example of a Kernel Algorithm
17:36	An Example of a Kernel Algorithm,ctd.(44)
18:21	An Example of a Kernel Algorithm,
18:31	An Example of a Kernel Algorithm,ctd.(44),
18:59	An Example of a Kernel Algorithm,,
19:26	An Example of a Kernel Algorithm,ctd.(44),,
19:34	An Example of a Kernel Algorithm,,,
36:25	An Example of a Kernel Algorithm,ctd.(44),,,
45:33	An Example of a Kernel Algorithm, ctd.(44).
47:37	Example:All Degree 2 Monominals.
50:13	An Example of a Kernel Algorithm,ctd.
50:14	An Example of a Kernel Algorithm,ctd(44)..
50:15	An Example of a Kernel Algorithm,.,
50:21	An Example of a Kernel Algorithm,ctd.(44),..,
50:22	An Example of a Kernel Algorithm,ctd.,,.
50:23	Example:All Degree 2 Monominals,
50:34	General Product Feature Space,,
51:30	Example:All Degree 2 Monominals,,
51:38	General Product Feature Space,
52:08	The Kernel Trick, N=d=2
52:22	Example:All Degree 2 Monominals,,,
52:28	The Kernel Trick, N=d=2 ,
55:25	The Kernel Trick,II..
55:41	The Kernel Trick,N=d=2,,
55:45	The Kernel Trick,II,
57:27	Mercer\"s Theorem
59:17	The Mercer Feature Map,.
59:33	Mercer\"s Theorem..
59:42	The Mercer Feature Map..
60:33	The Kernel Trick-Summary.,

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