Sciweavers

Share
ESANN
2004

Regularizing generalization error estimators: a novel approach to robust model selection

9 years 11 months ago
Regularizing generalization error estimators: a novel approach to robust model selection
Abstract. A well-known result by Stein shows that regularized estimators with small bias often yield better estimates than unbiased estimators. In this paper, we adapt this spirit to model selection, and propose regularizing unbiased generalization error estimators for stabilization. We trade a small bias in a model selection criterion against a larger variance reduction which has the beneficial effect of being more precise on a single training set.
Masashi Sugiyama, Motoaki Kawanabe, Klaus-Robert M
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2004
Where ESANN
Authors Masashi Sugiyama, Motoaki Kawanabe, Klaus-Robert Müller
Comments (0)
books