UC Santa Barbara
Generalized mixture models, semi-supervised learning, and unknown class inference
- Author(s): Frame, SJ
- Jammalamadaka, SR
- et al.
Published Web Locationhttps://doi.org/10.1007/s11634-006-0001-9
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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