Duvenaud, D and Lloyd, JR and Grosse, R and Tenenbaum, JB and Ghahramani, Z (2013) Structure Discovery in Nonparametric Regression through Compositional Kernel Search.
Full text not available from this repository.Abstract
Despite its importance, choosing the structural form of the kernel in nonparametric regression remains a black art. We define a space of kernel structures which are built compositionally by adding and multiplying a small number of base kernels. We present a method for searching over this space of structures which mirrors the scientific discovery process. The learned structures can often decompose functions into interpretable components and enable long-range extrapolation on time-series datasets. Our structure search method outperforms many widely used kernels and kernel combination methods on a variety of prediction tasks.
| Item Type: | Article |
|---|---|
| Additional Information: | 9 pages, 7 figures, To appear in proceedings of the 2013 International Conference on Machine Learning |
| Subjects: | UNSPECIFIED |
| Divisions: | Div F > Computational and Biological Learning |
| Depositing User: | Cron Job |
| Date Deposited: | 12 Mar 2013 17:10 |
| Last Modified: | 20 May 2013 01:41 |
| DOI: |
Actions (login required)
| View Item |
