  In this work we investigate the relationship between Bregman distances and
regularized Logistic Regression model. We present a detailed study of Bregman
Distance minimization, a family of generalized entropy measures associated with
convex functions. We convert the L1-regularized logistic regression into this
more general framework and propose a primal-dual method based algorithm for
learning the parameters. We pose L1-regularized logistic regression into
Bregman distance minimization and then apply non-linear constrained
optimization techniques to estimate the parameters of the logistic model.
