Some Properties of Empirical Risk Minimization over Donsker Classes
We study properties of algorithms which minimize (or almost minimize) empirical error over a Donsker class of functions. We show that the L2-diameter of the set of almost-minimizers is converging to zero in probability. Therefore, as the number of samples grows, it is becoming unlikely that adding a...
Main Authors: | Caponnetto, Andrea, Rakhlin, Alexander |
---|---|
Language: | en_US |
Published: |
2005
|
Subjects: | |
Online Access: | http://hdl.handle.net/1721.1/30545 |
Similar Items
-
Penalized empirical likelihood inference for sparse additive hazards regression with a diverging number of covariates
by: Wang, Shanshan, et al.
Published: (2016) -
Multiple-instance learning from unlabeled bags with pairwise similarity
by: Feng, Lei, et al.
Published: (2023) -
Weighted Statistic in Detecting Faint and Sparse Alternatives for High-Dimensional Covariance Matrices
by: Yang, Qing, et al.
Published: (2017) -
On SURE-Type Double Shrinkage Estimation
by: Jing, Bing-Yi, et al.
Published: (2017) -
The Hidden Costs of Not-So-Friendly Ghost Lanes
by: Acocella, Angela, et al.
Published: (2022)