Name
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.neunet.2015.02.006 | Neural Networks |
Keywords | DocType | Volume |
Statistical learning theory,Performance estimation,Local Rademacher Complexity,Unlabeled samples | Journal | 65 |
Issue | ISSN | Citations |
1 | 0893-6080 | 2 |
PageRank | References | Authors |
0.39 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luca Oneto | 1 | 830 | 63.22 |
| Alessandro Ghio | 2 | 667 | 35.71 |
| Sandro Ridella | 3 | 677 | 140.62 |
| Davide Anguita | 4 | 1001 | 70.58 |