Two new papers in the Journal of Machine Learning Research

[David 'Pablo' Cohn]

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The Journal of Machine Learning is pleased to announce the availability of 
two papers in electronic form.

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Learning with Mixtures of Trees
Marina Meila and Michael I. Jordan.
Journal of Machine Learning Research 1 (October 2000) pp. 1-48.

Abstract
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.

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Dependency Networks for Inference, Collaborative Filtering, and Data 
Visualization
David Heckerman, David Maxwell Chickering, Christopher Meek, Robert 
Rounthwaite, and Carl Kadie.
Journal of Machine Learning Research 1 (October 2000), pp. 49-75.

Abstract
We describe a graphical model for probabilistic relationships--an 
alternative to the Bayesian network--called a dependency network. The graph 
of a dependency network, unlike a Bayesian network, is potentially cyclic. 
The probability component of a dependency network, like a Bayesian network, 
is a set of conditional distributions, one for each node given its parents. 
We identify several basic properties of this representation and describe a 
computationally efficient procedure for learning the graph and probability 
components from data. We describe the application of this representation to 
probabilistic inference, collaborative filtering (the task of predicting 
preferences), and the visualization of acausal predictive relationships.


These first two papers of Volume 1 are available at http://www.jmlr.org in 
PostScript, PDF and HTML formats; a bound, hardcopy edition of Volume 1 
will be available in the next year.

-David Cohn, <david.cohn at acm.org>
  Managing Editor, Journal of Machine Learning Research

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