Bayesian Nonexhaustive Learning for Online Discovery and Modeling of Emerging Classes
We present a framework for online inference in the presence of a nonexhaustively defined set of classes that incorporates supervised classification with class discovery and modeling. A Dirichlet process prior (DPP) model defined over class distributions ensures that both known and unknown class dist...
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| creator | Dundar, Murat Akova, Ferit Qi, Alan Rajwa, Bartek |
| description | We present a framework for online inference in the presence of a nonexhaustively defined set of classes that incorporates supervised classification with class discovery and modeling. A Dirichlet process prior (DPP) model defined over class distributions ensures that both known and unknown class distributions originate according to a common base distribution. In an attempt to automatically discover potentially interesting class formations, the prior model is coupled with a suitably chosen data model, and sequential Monte Carlo sampling is used to perform online inference. Our research is driven by a biodetection application, where a new class of pathogen may suddenly appear, and the rapid increase in the number of samples originating from this class indicates the onset of an outbreak. |
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| subjects | Bayesian analysis Computer simulation Dirichlet problem Distance learning Inference Machine learning Modelling Outbreaks |
| title | Bayesian Nonexhaustive Learning for Online Discovery and Modeling of Emerging Classes |
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