Minimum Error Tree Decomposition
Artificial Intelligence
2013-04-05 v1
Abstract
This paper describes a generalization of previous methods for constructing tree-structured belief network with hidden variables. The major new feature of the described method is the ability to produce a tree decomposition even when there are errors in the correlation data among the input variables. This is an important extension of existing methods since the correlational coefficients usually cannot be measured with precision. The technique involves using a greedy search algorithm that locally minimizes an error function.
Cite
@article{arxiv.1304.1103,
title = {Minimum Error Tree Decomposition},
author = {L. Liu and Y. Ma and D. Wilkins and Z. Bian and X. Ying},
journal= {arXiv preprint arXiv:1304.1103},
year = {2013}
}
Comments
Appears in Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence (UAI1990)