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JMLR
2000

Learning with Mixtures of Trees

9 years 11 months ago
Learning with Mixtures of Trees
This paper describes the mixtures-of-trees model, a probabilistic model for discrete multidimensional domains. Mixtures-of-trees generalize the probabilistic trees of Chow and Liu (1968) in a different and complementary direction to that of Bayesian networks. We present efficient algorithms for learning mixtures-of-trees models in maximum likelihood and Bayesian frameworks. We also discuss additional efficiencies that can be obtained when data are "sparse," and we present data structures and algorithms that exploit such sparseness. Experimental results demonstrate the performance of the model for both density estimation and classification. We also discuss the sense in which tree-based classifiers perform an implicit form of feature selection, and demonstrate a resulting insensitivity to irrelevant attributes.
Marina Meila, Michael I. Jordan
Added 19 Dec 2010
Updated 19 Dec 2010
Type Journal
Year 2000
Where JMLR
Authors Marina Meila, Michael I. Jordan
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