Translator Disclaimer
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

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.

Information

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

MathSciNet: MR2459180

Digital Object Identifier: 10.1214/074921707000000067

Subjects:
Primary: 62-02, 62J12

Rights: Copyright © 2007, Institute of Mathematical Statistics

CHAPTER
16 PAGES


SHARE
Back to Top