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VOL. 54 | 2007 A flexible Bayesian generalized linear model for dichotomous response data with an application to text categorization
Susana Eyheramendy, David Madigan

Editor(s) Regina Liu, William Strawderman, Cun-Hui Zhang


We present a class of sparse generalized linear models that include probit and logistic regression as special cases and offer some extra flexibility. We provide an EM algorithm for learning the parameters of these models from data. We apply our method in text classification and in simulated data and show that our method outperforms the logistic and probit models and also the elastic net, in general by a substantial margin.


Published: 1 January 2007
First available in Project Euclid: 4 December 2007

MathSciNet: MR2459180

Digital Object Identifier: 10.1214/074921707000000067

Primary: 62-02 , 62J12

Keywords: binary regression , generalized linear model , text classification

Rights: Copyright © 2007, Institute of Mathematical Statistics


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