Name
Abstract | ||
|---|---|---|
In this paper, we face the problem of collecting training samples for regression problems under an active learning perspective. In particular, we propose various active learning strategies specifically developed for regression approaches based on Gaussian processes (GPs) and support vector machines (SVMs). For GP regression, the first two strategies are based on the idea of adding samples that are... |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TGRS.2012.2187906 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Field | DocType |
Training,Support vector machines,Mathematical model,Estimation,Current measurement,Vectors,Remote sensing | Feature vector,Least squares support vector machine,Principal component regression,Computer science,Regression analysis,Nonparametric regression,Support vector machine,Artificial intelligence,Relevance vector machine,Analysis of covariance,Machine learning | Journal |
Volume | Issue | ISSN |
50 | 10 | 0196-2892 |
Citations | PageRank | References |
12 | 0.56 | 25 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edoardo Pasolli | 1 | 285 | 17.04 |
| Farid Melgani | 2 | 1100 | 80.98 |
| Naif Alajlan | 3 | 839 | 50.51 |
| Yakoub Bazi | 4 | 672 | 43.66 |