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.
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.
|Additional Information:||9 pages, 7 figures, To appear in proceedings of the 2013 International Conference on Machine Learning|
|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|
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