Semisupervised Learning Approaches
author:
Tom Mitchell,
Carnegie Mellon University
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| Slides | |
| 0:00 | Semi-Supervised Learning over Text |
| 1:24 | Statistical learning methods |
| 4:14 | Semi-supervised Document classification |
| 4:38 | Document Classification: Bag of Words Approach |
| 5:04 | Twenty NewsGroups |
| 6:42 | What if we have labels for only some documents? |
| 12:48 | Nigam et al. |
| 14:49 | E-step, M-step |
| 16:20 | Using one labeled example per class |
| 20:37 | 20 Newgroups - 1 |
| 24:18 | 20 Newgroups - 2 |
| 24:20 | Downweight the influence of unlabeled examples by factor lambda |
| 24:21 | Why/When will this work? |
| 35:39 | EM for Semi-Supervised Doc Classification |
| 35:41 | Using Redundantly Predictive Features |
| 37:25 | Redundantly Predictive Features |
| 40:20 | Co-Training - 1 |
| 42:59 | CoTraining Algorithm #1 |
| 44:02 | - Redundantly Predictive Features - Part 2 |
| 45:23 | CoTraining: Experimental Results |
| 46:05 | CoTraining setting |
| 46:45 | Co-Training Rote Learner |
| 53:51 | Expected Rote CoTraining error given m examples |
| 54:50 | - CoTraining setting - Part 2 |
| 55:40 | - Co-Training Rote Learner - Part 2 |
| 59:11 | What to Know |
| 60:29 | Further Reading - 1 |
| 60:44 | Further Reading - 2 |
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Really a nice lecture!
Thanks a lot for uploading it.
hmm....very interesting for first timers in semi-supervised learning, but i was also expecting more on worse case scenario in both EM and Co-Training approaches he discussed. Wished he had more time to speak, still a great start....
The talk was very well done. The use of co-training seems artificial to me, however. By segregating the feature sets into 2 groups, no new information has been created. Why not just train a classifier using all of the features? It's not like the labels are only known for one classifier or the other. If that was true, there wouldn't be any way for classifier one to help classifier two.