Difference between revisions of "Class Meeting for 10-710 09-27-2011"
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This is one of the class meetings on the [[Syllabus for Structured Prediction 10-710 in Fall 2011|schedule]] for the course [[Syllabus for Structured Prediction 10-710 in Fall 2011|Syllabus for Structured Prediction 10-210 in Fall 2011]]. | This is one of the class meetings on the [[Syllabus for Structured Prediction 10-710 in Fall 2011|schedule]] for the course [[Syllabus for Structured Prediction 10-710 in Fall 2011|Syllabus for Structured Prediction 10-210 in Fall 2011]]. | ||
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=== Meta-Learning: Stacking and Sequential Models === | === Meta-Learning: Stacking and Sequential Models === | ||
Revision as of 14:42, 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
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.
