Name
Abstract | ||
|---|---|---|
This letter presents two kernel-based methods for semisupervised regression. The methods rely on building a graph or hypergraph Laplacian with both the available labeled and unlabeled data, which is further used to deform the training kernel matrix. The deformed kernel is then used for support vector regression (SVR). Given the high computational burden involved, we present two alternative formula... |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/LGRS.2008.2009077 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Parameter estimation,Support vector machines,Kernel,Neural networks,Remote monitoring,Spatial resolution,Laplace equations,Testing,Hyperspectral sensors,Hyperspectral imaging | Kernel (linear algebra),Pattern recognition,Incomplete Cholesky factorization,Computer science,Support vector machine,Hyperspectral imaging,Artificial intelligence,Estimation theory,Artificial neural network,Kernel method,Cholesky decomposition | Journal |
Volume | Issue | ISSN |
6 | 2 | 1545-598X |
Citations | PageRank | References |
18 | 1.73 | 11 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gustavo Camps-Valls | 1 | 2011 | 114.02 |
| Jordi Muñoz-Marí | 2 | 559 | 40.11 |
| L. G'omez-Chova | 3 | 181 | 13.79 |
| Katja Richter | 4 | 67 | 7.40 |
| Javier Calpe-Maravilla | 5 | 92 | 11.69 |