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

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URL: http://digitalcommons.calpoly.edu/stat_fac/25