Variational maximization-maximization of Bayesian mixture models and application to unsupervised image classification
This thesis mainly propose variational inference for Bayesian mixture models and their applications to solve machine learning problems. The mixture models addressed are the Gaussian mixture model (GMM), Dirichlet process mixture (DPM), the sparse coding based Gaussian mixture model (sGMM) and the Fi...
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| Format: | Thesis |
| Language: | English |
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2018
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| Online Access: | http://hdl.handle.net/10356/73199 |