Tutorial on Machine Learning Reductions
author:
John Langford,
TTI
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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bouncy zoomy camera work ruins an otherwise interesting lecture.