Boosting
author:
Gunnar Rätsch,
Max Planck Institute
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| Slides | |
| 1:03 | An Introducting to Boosting |
| 1:35 | Contents of this Course |
| 2:53 | Sources of Information |
| 4:40 | Learning - Problem Formulation I |
| 6:09 | Learning - Problem Formulation II |
| 7:50 | Example: natural Apples vs. plastic Apples |
| 8:00 | Example: natural Apples vs. plastic Apples. |
| 8:20 | Example: natural Apples vs. plastic Apples.. |
| 8:32 | Simple Hypotheses: Cuts on Coordinate Axes |
| 9:04 | PAC Learning |
| 11:31 | Levels of Generality |
| 13:25 | Weak and Strong Learning |
| 15:27 | 2. Basic Issues in Boosting |
| 16:04 | Simple Hypotheses: Cuts on Coordinate Axes. |
| 16:24 | AdaBoost (Freund&Schapire 1996) |
| 16:42 | Boosting: 1st Iteration (simple hypothesis) |
| 16:55 | AdaBoost (Freund&Schapire 1996). |
| 17:43 | Boosting: 1st Iteration (simple hypothesis). |
| 17:48 | AdaBoost (Freund&Schapire 1996).. |
| 17:59 | Boosting: 1st Iteration (simple hypothesis).. |
| 18:18 | Boosting:Recompute weighting |
| 18:33 | Boosting: 2nd Iteration |
| 18:40 | Boosting: 2nd Hypothesis |
| 18:45 | Boosting: Recompute weighting. |
| 18:59 | Boosting: 3rd Hypothesis |
| 19:11 | Boosting: 4th Hypothesis |
| 19:23 | Combination of Hypotheses |
| 19:44 | Decision |
| 20:03 | Decision. |
| 20:05 | Ensemble learning for Classification |
| 21:32 | Early Algorithms |
| 23:22 | AdaBoost algorithm |
| 25:52 | Desirable Properties of the Weak Learner |
| 28:05 | Weak Learners used with Boosting |
| 29:18 | Hypothesis weight... |
| 37:47 | Hypothesis weight,... |
| 38:45 | Reweighting... |
| 40:35 | Reweighting,... |
| 42:13 | What is going to come in the next Session? |
| 43:18 | Some Highlights |
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