  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.
