Generalized mixture models, semi-supervised learning, and unknown class inference

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 semi-supervised learning methods, we provide the generalization and discuss its computational implementation using three-stage expectation–maximization algorithm.

• Type: Journal
• Year: 2007
• Where: ADAC
• Authors: Samuel J. Frame, Sreenivasa Rao Jammalamadaka
• Keywords: ADAC 2007, Semi-supervised Learning, Standard Mixture Modeling, Unknown Class Inference