Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6863291 | Neural Networks | 2015 | 11 Pages |
Abstract
We derive in this paper a new Local Rademacher Complexity risk bound on the generalization ability of a model, which is able to take advantage of the availability of unlabeled samples. Moreover, this new bound improves state-of-the-art results even when no unlabeled samples are available.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Luca Oneto, Alessandro Ghio, Sandro Ridella, Davide Anguita,