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Open Access Publications from the University of California

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

  • Author(s): Frame, SJ
  • Jammalamadaka, SR
  • et al.

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. © Springer-Verlag 2007.

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