Work

Bayesian Nonexhaustive Learning for Online Discovery and Modeling of Emerging Classes

Year: 2012

Type: preprint

Abstract: 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1206.4600

Authors: Dundar, Murat, Akova, Ferit, Qi, Alan, Rajwa, Bartek

Institutions: Indiana University Indianapolis, Indiana University – Purdue University Indianapolis, Purdue University West Lafayette

Language: English

Topic: Bayesian Methods and Mixture Models

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2119518945

Cites: 12

Cited by: 18

Related to: 10