Difference between revisions of "Class Meeting for 10-710 09-27-2011"
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=== Meta-Learning: Stacking and Sequential Models === | === Meta-Learning: Stacking and Sequential Models === | ||
| − | * [http://www.cs.cmu.edu/~wcohen/10-707/09-27-crfs+stacking.ppt Slides] | + | * [http://www.cs.cmu.edu/~wcohen/10-707/09-27-crfs+stacking.ppt Slides on stacking] |
| + | * [http://www.cs.cmu.edu/~wcohen/10-707/09-29-searn.ppt Slides on Searn] | ||
| + | |||
=== Required Readings === | === Required Readings === | ||
* [[required::cohen_2005_stacked_sequential_learning | {{MyCiteconference| booktitle = International Joint Conference on Artificial Intelligence| coauthors = Vitor Carvalho| date = 2005| first = William W.| last = Cohen| pages = 671-676| title = Stacked sequential learning}}]]. | * [[required::cohen_2005_stacked_sequential_learning | {{MyCiteconference| booktitle = International Joint Conference on Artificial Intelligence| coauthors = Vitor Carvalho| date = 2005| first = William W.| last = Cohen| pages = 671-676| title = Stacked sequential learning}}]]. | ||
| + | * [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| + | * [http://hal3.name/docs/daume06searn-practice.pdf Searn in Practice] Unpublished manuscript, Daume et al.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 === | ||
| Line 16: | Line 20: | ||
* [http://hal3.name/docs/daume09searn.pdf Search-based Structured Prediction, Daume, Langford, and Marcu, Machine Learning Journal (2009)]. Another clever meta-learning algorithm that works well for sequences. | * [http://hal3.name/docs/daume09searn.pdf Search-based Structured Prediction, Daume, Langford, and Marcu, Machine Learning Journal (2009)]. Another clever meta-learning algorithm that works well for sequences. | ||
* [http://learning.eng.cam.ac.uk/zoubin/papers/CGM.pdf Conditional graphical models, Perez-Cruz & Ghahramani, 2007, in Predicting Structured Data. MIT Press, Cambridge, MA, USA, pp. 265-282.]. A very simple and effective meta-learning method. | * [http://learning.eng.cam.ac.uk/zoubin/papers/CGM.pdf Conditional graphical models, Perez-Cruz & Ghahramani, 2007, in Predicting Structured Data. MIT Press, Cambridge, MA, USA, pp. 265-282.]. A very simple and effective meta-learning method. | ||
| + | * [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. | ||
Revision as of 14:44, 19 July 2011
This is one of the class meetings on the schedule for the course Syllabus for Structured Prediction 10-210 in Fall 2011.
Meta-Learning: Stacking and Sequential Models
Required Readings
- Stacked sequential learning, by William W. Cohen, Vitor Carvalho. In International Joint Conference on Artificial Intelligence, 2005..
- 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
- An Effective Two-Stage Model for Exploiting Non-Local Dependencies in Named Entity Recognition, Krishnan and Manning, ACL 2006. Another take on stacked sequential learning.
- Zhenzhen Kou and William W. Cohen (2007): Stacked Graphical Models for Efficient Inference in Markov Random Fields in SDM-2007. Extended version of the stacked sequential learning method that applies to arbitrary graphs.
- Transformation-Based Error-Driven Learning and Natural Language Processing, Brill, COLING 1995. The learning algorithm in the Brill parser, which has also been used for NER (e.g., in Abgene).
- Search-based Structured Prediction, Daume, Langford, and Marcu, Machine Learning Journal (2009). Another clever meta-learning algorithm that works well for sequences.
- Conditional graphical models, Perez-Cruz & Ghahramani, 2007, in Predicting Structured Data. MIT Press, Cambridge, MA, USA, pp. 265-282.. A very simple and effective meta-learning method.
- 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.
