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High-Dimensional Bayesian Classifiers

David Madigan
Rutgers University
dmadigan@rutgers.edu
Supervised learning applications in text categorization, authorship attribution, hospital profiling, and many other areas frequently involve training data with more predictors than examples. Regularized logistic models often prove useful in such applications and I will present some experimental results. A Bayesian interpretation of regularization offers advantages. In applications with small numbers of training examples, incorporation of external knowledge via informative priors proves highly effective. Sequential learning algorithms also emerge naturally in the Bayesian approach. Finally I will discuss some recent ideas concerning hyperparamter selection.

(joint work with Alex Genkin and David D. Lewis)

Short Course: Information Theory & Statistics
Bin Yu & Mark Hansen
June 1, 2005
Colorado State University Campus
Fort Collins, CO 80523

Graybill Conference
June 2-3, 2005
Hilton Fort Collins

(Formerly: University Park Holiday- Inn)
Fort Collins, CO 80526

www.stat.colostate.edu/graybillconference
Graybill Conference Poster

Last Updated: Friday, May 24, 2005