BayesANIL - A Bayesian Model for Handling Approximate, Noisy or Incomplete Labeling in Text Classification
author:
Ganes Ramakrishnan,
IBM
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| Slides | |
| 0:06 | BayesANIL A Bayesian Model for Handling Approximate, Noisy or Incomplete Labeling in Text Classification |
| 0:25 | Outline |
| 0:37 | Motivation - hurdles in supervised learning of text classifiers |
| 1:29 | Related work |
| 2:52 | Related work (contd.) |
| 4:00 | What we propose |
| 5:09 | Role of BayesANIL in text classification |
| 7:27 | The BayesANIL model : notations |
| 8:30 | The BayesANIL model : notations… |
| 9:47 | The BayesANIL model: Objective function |
| 10:33 | The BayesANIL model: E and M Steps |
| 11:09 | The Algorithm |
| 12:42 | Re-estimating the empirical distribution |
| 14:09 | Utilizing parameters of BayesANIL in NB |
| 14:58 | Utilizing parameters of BayesANIL in SVM |
| 16:00 | Experiments and Results |
| 17:03 | Experiments and Results: Supervised |
| 18:23 | Experiments and Results: Labeled-unlabeled |
| 19:20 | Experiments and Results: Access to unlabeled for WebKB |
| 20:18 | Experiments and Results: Access to unlabeled for 20 Newsgroups |
| 21:42 | Experiments and Results: Noisy Labels |
| 23:21 | Comparison with results as reported by (Bing Liu et al 2003) |
| 24:05 | Experiments and Results: Notion of Support |
| 25:13 | Summary |
| 26:25 | Future work |
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