Name
Abstract | ||
|---|---|---|
Gaussian process classification is a popular method with a number of appealing properties. We show how to scale the model within a variational inducing point framework, outperforming the state of the art on benchmark datasets. Importantly, the variational formulation can be exploited to allow classification in problems with millions of data points, as we demonstrate in experiments. |
Year | Venue | Field |
|---|---|---|
2015 | JMLR Workshop and Conference Proceedings | Data point,Data mining,Mathematical optimization,Computer science,Gaussian process,Scalability |
DocType | Volume | ISSN |
Conference | 38 | 1938-7288 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| James Hensman | 1 | 265 | 20.05 |
| Alexander Matthews | 2 | 26 | 2.06 |
| Zoubin Ghahramani | 3 | 10455 | 1264.39 |