Machine Learning Summer School 2005 - Chicago

Introduction to Kernel Methods

author: Mikhail Belkin, Department of Computer Science and Engineering, Ohio State University

Categories

Top: Computer Science: Machine Learning: Kernel Methods

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*Slides*
0:01	Introduction to Kernel Methods II
2:13	Kernel-based algorithms
3:16	Regression/Classification
6:25	Example of regression_
10:19	Example of regression
10:44	Regularization
14:50	RKHS as smoothness penalty
16:53	Kernel classification/regression
18:43	Representer theorem
21:40	Reproducing property
23:30	Proof of representer theorem I
27:02	Proof of representer theorem II
30:24	Proof of representer theorem III
32:04	Proof of representer theorem IV
35:03	Algorithms: RLS
36:52	RLS demo
40:26	Algorithms: RLS_
44:50	Support Vector Machines
47:26	Support Vector Machines: Sparsity
48:00	Support Vector Machines: Sparsity
48:11	Support Vector Machines: Sparsity
49:09	Support Vector Machines: Sparsity
50:07	Support Vector Machines: Sparsity
51:34	Feature map interpretation
52:47	Feature map: RLS
55:45	Generalization error
58:00	Generalization bound
60:15	Some References

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