Machine Learning Summer School 2004 - Berder Island

Statistical Learning Theory

author: John Shawe-Taylor, University of London

Categories

Top: Computer Science: Machine Learning: Statistical Learning

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*Slides*
1:06	Basic Statistical Learning Theory
5:26	STRUCTURE
7:41	Aim:
8:25	What won't be included
9:37	Theories of learning
11:52	Theories of learning cont.
13:43	General statistical considerations
15:41	General statistical considerations cont.
17:57	Generalisation of a learner
20:07	Generalisation of a learner,
21:21	Generalisation of a learner.
28:02	Generalisation of a learner,.
29:00	Example of Generalisation I
30:15	Example of Generalisation II
32:21	Example of Generalisation III
34:06	Error distribution: full dataset
34:29	Error distribution: dataset size: 342
35:19	Error distribution: dataset size: 273
35:23	Error distribution: dataset size: 205
35:29	Error distribution: dataset size: 137
35:45	Error distribution: dataset size: 68
36:00	Error distribution: dataset size: 34
36:06	Error disribution: dataset size: 27
36:08	Error distribution: dataset size:20
36:34	Error distribution: dataset size: 14
36:38	Error distribution: dataset size: 7
36:42	Bayes risk and consistency
39:25	Expected versus confident bounds
40:12	Expected versus confident bounds cont.
40:38	Error distribution: dataset size: 7.
41:51	Error distribution: dataset size: 14.
42:10	Expected versus confident bounds cont..
43:53	Probability of being misled in classification

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