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Introduction to Kernel Methods
Published on Feb 25, 200714741 Views
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Chapter list
Introduction to Kernel Methods II00:01
Kernel-based algorithms02:13
Regression/Classification03:16
Example of regression_06:25
Example of regression10:19
Regularization10:44
RKHS as smoothness penalty14:50
Kernel classification/regression16:53
Representer theorem18:43
Reproducing property21:40
Proof of representer theorem I23:30
Proof of representer theorem II27:02
Proof of representer theorem III30:24
Proof of representer theorem IV32:04
Algorithms: RLS35:03
RLS demo36:52
Algorithms: RLS_40:26
Support Vector Machines44:50
Support Vector Machines: Sparsity47:26
Support Vector Machines: Sparsity48:00
Support Vector Machines: Sparsity48:11
Support Vector Machines: Sparsity49:09
Support Vector Machines: Sparsity50:07
Feature map interpretation51:34
Feature map: RLS52:47
Generalization error55:45
Generalization bound58:00
Some References01:00:15