Name
Abstract | ||
|---|---|---|
This paper proposes a forward adaptive transfer learning method, referred tp as forward adaptive transfer learning for Gaussian process regression, which allows robots to leverage previously learned Gaussian process regression models and use them as sources of information in new learning tasks. This is especially valuable when limited training data are available for the new target task. Forward adaptive transfer learning for Gaussian process regression decouples the kernel and hyperparameter selection for the target task from those of the source task, providing an inference framework that is desirable when dealing with real-world dynamic environments. Finally, because the source task has been learned a priori, the computational complexity of forward adaptive transfer learning for Gaussian process regression is lower as compared to other semiparametric Gaussian process approaches using transfer kernels. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.2514/1.I010437 | JOURNAL OF AEROSPACE INFORMATION SYSTEMS |
Field | DocType | Volume |
Kriging,Leverage (finance),Transfer of learning,Real-time computing,Artificial intelligence,Engineering,Robot | Journal | 14 |
Issue | ISSN | Citations |
4 | 1940-3151 | 5 |
PageRank | References | Authors |
0.42 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Neeti Wagle | 1 | 21 | 2.66 |
| Eric W. Frew | 2 | 182 | 26.73 |