A flexible Bayesian generalized linear model for dichotomous response data with an application to text categorization

Details A flexible Bayesian generalized linear model for dichotomous response data with an application to text categorization A flexible Bayesian generalized linear model for dichotomous response data with an application to text categorization

2007-08-07

arxiv.org/abs/0708.0959v1

IMS Lecture Notes Monograph Series 2007, Vol. 54, 76-91

Statistics

Methodology

Published at http://dx.doi.org/10.1214/074921707000000067 in the IMS Lecture Notes Monograph Series (http://www.imstat.org/p

Scientific paper

10.1214/074921707000000067

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.

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