1. A machine implemented system that facilitates maximizing probabilities comprising: a data input component that provides one or more types of data for analysis; an analysis component that analyzes at least a subset of the one or more types of data to compute maximized probabilities by employing an improved iterative scaling function, a plurality of Exponential priors that respectively correspond to a plurality of different features, and at least one of a LaPlacian prior, a non-Gaussian distribution, or an alternative iterative scaling function; And a training component that computes one or more weights for respective test data based at least in part on the maximized probabilities.