In this paper, we discuss Generalized Mixture Models and related Semi-supervised learning methods, and show how they can be used to provide explicit methods for unknown class inference. After a brief description of standard mixture modeling and current model-based semisupervised learning methods, we provide the generalization and discuss its computational implementation using 3-stage Expectation-Maximization algorithm.