Institute of Mathematical Statistics Lecture Notes - Monograph Series

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

Susana Eyheramendy and David Madigan

Full-text: Open access

Abstract

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.

Chapter information

Source
Regina Liu, William Strawderman and Cun-Hui Zhang, eds., Complex Datasets and Inverse Problems: Tomography, Networks and Beyond (Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2007), 76-91

Dates
First available: 4 December 2007

Permanent link to this document
http://projecteuclid.org/euclid.lnms/1196794944

Digital Object Identifier
doi:10.1214/074921707000000067

Subjects
Primary: 62-02: Research exposition (monographs, survey articles) 62J12: Generalized linear models

Keywords
Generalized linear model text classification binary regression

Citation

Eyheramendy, Susana; Madigan, David. A flexible Bayesian generalized linear model for dichotomous response data with an application to text categorization. Complex Datasets and Inverse Problems, 76--91, Institute of Mathematical Statistics, Beachwood, Ohio, USA, 2007. doi:10.1214/074921707000000067. http://projecteuclid.org/euclid.lnms/1196794944.


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