Name
Title | ||
|---|---|---|
Bayesian Nonexhaustive Learning for Online Discovery and Modeling of Emerging Classes |
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. |
Year | Venue | DocType |
|---|---|---|
2012 | ICML | Journal |
Volume | Citations | PageRank |
abs/1206.4600 | 6 | 0.47 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Murat Dundar | 1 | 323 | 25.63 |
| Ferit Akova | 2 | 28 | 3.06 |
| yuan | 3 | 27 | 2.13 |
| Bartek Rajwa | 4 | 92 | 10.40 |