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A Flexible Bayesian Generalized Linear Model for Dichotomous Response Data with an Application to Text Categorization

Eyheramendy, Susana; Madigan, David B.

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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Title
Complex Datasets and Inverse Problems: Tomography, Networks and Beyond
Publisher
Institute of Mathematical Statistics
DOI
https://doi.org/10.1214/074921707000000067
URL
http://projecteuclid.org/euclid.lnms/1196794944

More About This Work

Academic Units
Statistics
Series
IMS Lecture Notes–Monograph Series, 54
Published Here
May 13, 2014