PAC-learning bounded tree-width Graphical Models

@inproceedings{Narasimhan2004PAClearningBT,
  title={PAC-learning bounded tree-width Graphical Models},
  author={Mukund Narasimhan and Jeff A. Bilmes},
  booktitle={UAI},
  year={2004}
}
We show that the class of strongly connected graphical models with treewidth at most k can be properly efficiently PAC-learnt with respect to the Kullback-Leibler Divergence. Previous approaches to this problem, such as those of Chow ([1]), and Hoffgen ([7]) have shown that this class is PAClearnable by reducing it to a combinatorial optimization problem. However, for k > 1, this problem is NPcomplete ([15]), and so unless P=NP, these approaches will take exponential amounts of time. Our… CONTINUE READING
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