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PRICAI
2000
Springer

The Lumberjack Algorithm for Learning Linked Decision Forests

13 years 7 months ago
The Lumberjack Algorithm for Learning Linked Decision Forests
While the decision tree is an effective representation that has been used in many domains, a tree can often encode a concept inefficiently. This happens when the tree has to represent a subconcept multiple times in different parts of the tree. In this paper we introduce a new representation based on trees, the linked decision forest, that does not need to repeat internal structure. We also introduce a supervised learning algorithm, Lumberjack, that uses the new representation. We then show empirically that Lumberjack improves generalization accuracy on hierarchically decomposable concepts.
William T. B. Uther, Manuela M. Veloso
Added 25 Aug 2010
Updated 25 Aug 2010
Type Conference
Year 2000
Where PRICAI
Authors William T. B. Uther, Manuela M. Veloso
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