EPSRC Winter School in Mathematics for Data Modelling

Kernel Based Methods

author: Colin Campbell, University of Bristol

Description

This second presentation covers more general kernel methods including training, model selection and practical aspects.

Categories

Top: Computer Science: Machine Learning: Kernel Methods

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*Slides*
0:00	Kernel Based Methods
0:01	- Questions
5:59	In the second lecture we will - 2
6:53	In the second lecture we will - 3
8:52	Overview - 1
10:33	Overview - 2
10:39	A Linear Programming Approach to Classification - 1
11:09	A Linear Programming Approach to Classification - 2
11:38	A Linear Programming Approach to Classification - 3
12:12	- Questions
13:11	Novelty Detection (one-class classifiers) - 1
17:27	- Questions
19:28	Novelty Detection (one-class classifiers) - 3
19:40	Novelty Detection (one-class classifiers) - 4
21:32	Novelty Detection (one-class classifiers) - 5
21:43	Novelty Detection (one-class classifiers) - 6
21:51	Novelty Detection (one-class classifiers) - 7
21:57	Novelty Detection (one-class classifiers) - 8
22:22	Novelty Detection (one-class classifiers) - 9
23:08	Novelty Detection (one-class classifiers) - 10
23:18	Novelty Detection (one-class classifiers) - 11
23:34	Different types of learning: passive vs active learning - 1
24:24	Different types of learning: passive vs active learning - 2
26:15	Different types of learning: passive vs active learning - 3
26:34	Different types of learning: passive vs active learning - 4
26:51	Different types of learning: passive vs active learning - 5
27:08	Different types of learning: passive vs active learning - 6
27:10	Different types of learning: passive vs active learning - 7
27:30	Different types of learning: passive vs active learning - 8
27:44	- Questions
31:58	Different types of learning: passive vs active learning - 10
32:00	Different types of learning: passive vs active learning - 11
33:14	Different types of learning: passive vs active learning - 12
33:26	Different types of learning: passive vs active learning - 13
36:26	Different types of learning: passive vs active learning - 14
36:39	Different types of learning: passive vs active learning - 15
36:49	Different types of learning: passive vs active learning - 16
37:54	Training SVMs and other kernel machines - 1
38:03	Training SVMs and other kernel machines - 2
38:55	Training SVMs and other kernel machines - 3
39:57	Training SVMs and other kernel machines - 4
40:20	Training SVMs and other kernel machines - 5
40:23	Training SVMs and other kernel machines - 6
40:27	Training SVMs and other kernel machines - 7
41:47	Training SVMs and other kernel machines - 8
42:55	Model selection and Kernel Messaging - 1
43:33	Model selection and Kernel Messaging - 2
44:27	- Questions
45:56	Model selection and Kernel Messaging - 4
46:09	Model selection and Kernel Messaging - 5
48:51	Data fusion using composite kernels - 1
50:21	Data fusion using composite kernels - 2
50:33	Data fusion using composite kernels - 3
50:51	Data fusion using composite kernels - 4
51:16	Data fusion using composite kernels - 5
52:21	Data fusion using composite kernels - 6
52:29	Data fusion using composite kernels - 7
52:39	Applications of SVMs - 1
52:46	Applications of SVMs - 2
53:53	Applications of SVMs - 3
54:10	Applications of SVMs - 4
54:25	Applications of SVMs - 5
55:02	Applications of SVMs - 6
55:04	Applications of SVMs - 7
56:12	Applications of SVMs - 8
56:59	Applications of SVMs - 9
57:21	Conclusion

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