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Introduction to Kernel Methods

author:Mikhail Belkin, Department of Computer Science and Engineering, Ohio State University
published: Feb. 25, 2007,   recorded: May 2005,   views: 634
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Slides

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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