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The Journal of Machine Learning is pleased to announce the availability of
two papers in electronic form.
- ----------------------------------------
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.
- ----------------------------------------
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@acm.org>
Managing Editor, Journal of Machine Learning Research
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