[Apologies for the broad distribution. Future announcements of this type will only be posted to [EMAIL PROTECTED] See the end of this message for information on subscribing.] 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, <[EMAIL PROTECTED]> Managing Editor, Journal of Machine Learning Research - ------- This message has been sent to the mailing list "[EMAIL PROTECTED]", which is maintained automatically by majordomo. To subscribe to the list, send mail to [EMAIL PROTECTED] with the line "subscribe jmlr-announce" in the body; to unsubscribe send email to [EMAIL PROTECTED] with the line "unsubscribe jmlr-announce" in the body.
