1. A method in a data processing system for generating a recognition data model set comprising: a computer receiving a first group of class dependent data models sets based on a corresponding second group of class training data sets, each set of training data originating from a subset of all training data; the computer receiving a class independent data model set based on all training data, each data model set comprising a plurality of data models, wherein each data model in one data model set is part of a group of corresponding data models from other data model sets; the computer comparing a fit of each group of corresponding class dependent data models to training data and the fit of a corresponding independent data model to the training data, wherein the fit is determined by measuring a difference between a) a first cross entropy of corresponding class dependent data models and the training data and b) a second cross entropy of a corresponding class independent data model and the training data; the computer, responsive to determining that a difference between the first cross entropy and the second cross entropy is insignificant, adding the corresponding class independent data model to a final data model set; and the computer, responsive to determining that the difference between the first cross entropy and the second cross entropy is significant, adding the corresponding class dependent data models to the final data model set.