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Bayesian Nonexhaustive Learning for Online Discovery and Modeling of Emerging Classes by Dundar, Murat; Akova, Ferit; Qi, Alan; Rajwa, Bartek is a scholarly article available to read on EtoBox.

What is Bayesian Nonexhaustive Learning for Online Discovery and Modeling of Emerging Classes about?

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.

Author

Dundar, Murat; Akova, Ferit; Qi, Alan; Rajwa, Bartek

Published

2012

Language

EN