1. A method of adapting a mixture-model-based classifier capable of partitioning a feature space, from a source domain with labeled data samples (measured on that feature space) on which the classifier was trained, to a target domain having unlabeled data and a limited set of labeled data, the method, executed on a computer processor operating on certain common data-sample features, comprising the steps of: providing a set of source domain classifier parameters; expressing the adaptation of the source domain classifier parameters to the target domain data in terms of a constrained optimization of an objective function that considers both a mixture model fitting of its component (latent) variables to the unlabeled data from the target domain and a measure of classifier accuracy on the labeled data from the target domain; providing an algorithm for execution on the computer to automatically optimize the objective function for successive constraint values on the classifier's accuracy on the labeled target domain data; and wherein the distribution of the latent variables is parametrically constrained so that feature-space partitioning implications are imposed by the limited set of labeled target domain data.