Title

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

Author Info

Samuel J. Frame, California Polytechnic State University, San Luis ObispoFollow
Sreenivasa Rao Jammalamadaka, University of California - Santa Barbara

Recommended Citation

Postprint version. Published in Advances in Data Analysis and Classification, Volume 1, Issue 1, March 1, 2007, pages 23-38.

The definitive version is available at https://doi.org/10.1007/s11634-006-0001-9.

Abstract

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.

Disciplines

Statistics and Probability

Copyright

2007 Springer.