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» Fast approximation of the bootstrap for model selection
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ESANN
2003
13 years 10 months ago
Fast approximation of the bootstrap for model selection
The bootstrap resampling method may be efficiently used to estimate the generalization error of a family of nonlinear regression models, as artificial neural networks. The main dif...
Geoffroy Simon, Amaury Lendasse, Vincent Wertz, Mi...
CSDA
2008
94views more  CSDA 2008»
13 years 9 months ago
Robust model selection using fast and robust bootstrap
Robust model selection procedures control the undue influence that outliers can have on the selection criteria by using both robust point estimators and a bounded loss function wh...
Matias Salibian-Barrera, Stefan Van Aelst
IWANN
2005
Springer
14 years 2 months ago
Input and Structure Selection for k-NN Approximator
Abstract. This paper presents k-NN as an approximator for time series prediction problems. The main advantage of this approximator is its simplicity. Despite the simplicity, k-NN c...
Antti Sorjamaa, Nima Reyhani, Amaury Lendasse
SMA
2008
ACM
162views Solid Modeling» more  SMA 2008»
13 years 9 months ago
Fast and robust bootstrap
The Least Trimmed Squares (LTS) estimator is a frequently used robust estimator of regression. When it comes to inference for the parameters of the regression model, the asymptoti...
Matias Salibian-Barrera, Stefan Van Aelst, Gert Wi...
ICML
2008
IEEE
14 years 10 months ago
Bolasso: model consistent Lasso estimation through the bootstrap
We consider the least-square linear regression problem with regularization by the 1-norm, a problem usually referred to as the Lasso. In this paper, we present a detailed asymptot...
Francis R. Bach