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VLDB
2006
ACM

Dependency trees in sub-linear time and bounded memory

14 years 2 months ago
Dependency trees in sub-linear time and bounded memory
We focus on the problem of efficient learning of dependency trees. Once grown, they can be used as a special case of a Bayesian network, for PDF approximation, and for many other uses. Given the data, a well-known algorithm can fit an optimal tree in time that is quadratic in the number of attributes and linear in the number of records. We show how to modify it to exploit partial knowledge about edge weights. Experimental results show running time that is near-constant in the number of records, without significant loss in accuracy of the generated trees. Keywords Data Mining Probably Approximately Correct Learning Fast Algorithms Dependency Trees
Dan Pelleg, Andrew W. Moore
Added 05 Dec 2009
Updated 05 Dec 2009
Type Conference
Year 2006
Where VLDB
Authors Dan Pelleg, Andrew W. Moore
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