Name
Abstract | ||
|---|---|---|
This paper tackles the problem of selecting among several linear estimators
in non-parametric regression; this includes model selection for linear
regression, the choice of a regularization parameter in kernel ridge regression
or spline smoothing, and the choice of a kernel in multiple kernel learning. We
propose a new algorithm which first estimates consistently the variance of the
noise, based upon the concept of minimal penalty which was previously
introduced in the context of model selection. Then, plugging our variance
estimate in Mallows' $C_L$ penalty is proved to lead to an algorithm satisfying
an oracle inequality. Simulation experiments with kernel ridge regression and
multiple kernel learning show that the proposed algorithm often improves
significantly existing calibration procedures such as 10-fold cross-validation
or generalized cross-validation. |
Year | Venue | Keywords |
|---|---|---|
2009 | NIPS | linear regression,simulation experiment,satisfiability,model selection,non parametric regression |
Field | DocType | Citations |
Mathematical optimization,Principal component regression,Kernel embedding of distributions,Nonparametric regression,Multiple kernel learning,Polynomial kernel,Variable kernel density estimation,Kernel regression,Mathematics,Kernel (statistics) | Conference | 8 |
PageRank | References | Authors |
0.79 | 17 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sylvain Arlot | 1 | 65 | 6.87 |
| Francis Bach | 2 | 11490 | 622.29 |