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Learning Tree Conditional Random Fields (2010)

By: Joseph K. Bradley and Carlos Guestrin

Abstract: We examine maximum spanning tree-based methods for learning the structure of tree Conditional Random Fields (CRFs) P(Y | X). We use edge weights which take advantage of local inputs X and thus scale to large problems. For a general class of edge weights, we give a negative learnability result. However, we demonstrate that two members of the class-local Conditional Mutual Information and Decomposable Conditional Influence-have reasonable theoretical bases and perform very well in practice. On synthetic data and a large-scale fMRI application, our methods outperform existing techniques.

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Joseph K. Bradley and Carlos Guestrin (2010). "Learning Tree Conditional Random Fields." International Conference on Machine Learning (ICML 2010). pdf   talk        
BibTeX citation

@inproceedings{bradley+guestrin:icml10crfs,
title = {Learning Tree Conditional Random Fields},
author = {Joseph K. Bradley and Carlos Guestrin},
booktitle = {International Conference on Machine Learning (ICML 2010)},
month = {June},
year = {2010},
address = {Haifa, Israel},
wwwfilebase = {icml2010-bradley-guestrin},
wwwtopic = {Structure Learning}
}



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