Location: EU Supported » PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning » Machine Learning Summer School 2004 - Berder Island

Statistical Learning Theory

author:John Shawe-Taylor, Centre for Computational Statistics and Machine Learning, University College London
published: Feb. 25, 2007,   recorded: September 2004,   views: 7947
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Slides

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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Reviews and comments:

Comment1 Diego, August 18, 2008 at 8:22 p.m.:

In slide 24, the rhs of the second equation is the expected loss using the Bayes risk

Comment2 vincent, January 6, 2009 at 10:10 a.m.:

part 4 of this lecture is actually part 1. Need proper part 4!

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