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Machine Learning Summer School 2005 - Chicago

Tutorial on Machine Learning Reductions

author: John Langford, Toyota Technological Institute at Chicago

Description

There are several different classification problems commonly encountered in real world applications such as 'importance weighted classification', 'cost sensitive classification', 'reinforcement learning', 'regression' and others. Many of these problems can be related to each other by simple machines (reductions) that transform problems of one type into problems of another type.

Finding a reduction from your problem to a more common problem allows the reuse of simple learning algorithms to solve relatively complex problems. It also induces an organization on learning problems — problems that can be easily reduced to each other are 'nearby' and problems which can not be so reduced are not close.

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Slides
0:03 Tutorial on...
3:23 Learning Problems ...
4:42 Reductions turn type ...
5:31 Why Learning Reductions?
5:47 It works
8:10 It`s modular
13:00 It`s reductionist
14:54 It`s easy
16:12 Classification Definition
18:58 Outline
19:41 Importance Weighted..
21:33 The core theorem
26:16 Distribution Transform..
30:20 Costing
32:21 Costing
35:51 Outline
36:19 Class Probability...
41:09 Reasons for ...
42:52 The Probing Method
45:55 The Probing Algorithm
47:44 The Probing Method
50:35 Comparison with ..
55:17 The one ...
60:32 Probing Theory
64:03 The proof
67:03 The proof
71:39 Proof II
75:44 Some Caveats

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Reviews and comments:

Comment1 Paul, April 5, 2007 at 2:59 a.m.:

bouncy zoomy camera work ruins an otherwise interesting lecture.


Comment2 Alex, July 15, 2008 at 6:33 p.m.:

Thanks for the interesting talk.


Comment3 hamid, February 14, 2009 at 2:25 p.m.:

every thing is satisfactory for someone who have not been thought by good profesors can find this information very
constructive for his trainig

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