Difference between revisions of "Class Meeting for 10-707 9/29/2010"
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=== Meta-Learning: Searn === | === Meta-Learning: Searn === | ||
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=== Required Readings === | === Required Readings === | ||
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| + | * [http://hal3.name/docs/daume09searn.pdf Search-Based Structured Prediction], Dame, Langford and Marcu. This is a journal-length paper, but it's not very dense. We will not be going through the theorems in any detail. | ||
| + | * [http://hal3.name/docs/daume06searn-practice.pdf Searn in Practice] Unpublished manuscript, Daume et al. | ||
=== Optional Readings === | === Optional Readings === | ||
| − | * to | + | * [http://hal3.name/docs/daume05laso.pdf Learning as Search Optimization: Approximate Large Margin Methods for Structured Prediction] An alternative formal analysis of Searn. |
| + | * [http://hal3.name/docs/daume09unsearn.pdf Unsupervised Search-based Structured Prediction]. Applying Searn to unsupervised and/or semi-supervised problems. | ||
Latest revision as of 15:49, 28 September 2010
This is one of the class meetings on the schedule for the course Information Extraction 10-707 in Fall 2010.
Meta-Learning: Searn
Required Readings
- Search-Based Structured Prediction, Dame, Langford and Marcu. This is a journal-length paper, but it's not very dense. We will not be going through the theorems in any detail.
- Searn in Practice Unpublished manuscript, Daume et al.
Optional Readings
- Learning as Search Optimization: Approximate Large Margin Methods for Structured Prediction An alternative formal analysis of Searn.
- Unsupervised Search-based Structured Prediction. Applying Searn to unsupervised and/or semi-supervised problems.
