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We present a class of sparse generalized linear models that include probitand logistic regression as special cases and offer some extra flexibility. Weprovide 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 thatour method outperforms the logistic and probit models and also the elastic net,in general by a substantial margin.

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