Uncertainty in Artificial Intelligence
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Minimum Error Tree Decomposition
L. Liu, Y. Ma, D. Wilkins, Z. Bian, X. Ying
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.
Keywords:
Pages: 180-185
PS Link:
PDF Link: /papers/90/p180-liu.pdf
BibTex:
@INPROCEEDINGS{Liu90,
AUTHOR = "L. Liu and Y. Ma and D. Wilkins and Z. Bian and X. Ying",
TITLE = "Minimum Error Tree Decomposition",
BOOKTITLE = "Proceedings of the Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-90)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "1990",
PAGES = "180--185"
}


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