Name
Title | ||
|---|---|---|
An improved analysis of the Rademacher data-dependent bound using its self bounding property. |
Abstract | ||
|---|---|---|
The problem of assessing the performance of a classifier, in the finite-sample setting, has been addressed by Vapnik in his seminal work by using data-independent measures of complexity. Recently, several authors have addressed the same problem by proposing data-dependent measures, which tighten previous results by taking in account the actual data distribution. In this framework, we derive some data-dependent bounds on the generalization ability of a classifier by exploiting the Rademacher Complexity and recent concentration results: in addition of being appealing for practical purposes, as they exploit empirical quantities only, these bounds improve previously known results. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.neunet.2013.03.017 | Neural Networks |
Keywords | DocType | Volume |
Error estimation,Data-dependent bounds,Rademacher complexity,Concentration of measure | Journal | 44 |
Issue | ISSN | Citations |
1 | 0893-6080 | 8 |
PageRank | References | Authors |
0.53 | 15 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luca Oneto | 1 | 830 | 63.22 |
| Alessandro Ghio | 2 | 667 | 35.71 |
| Davide Anguita | 3 | 1001 | 70.58 |
| Sandro Ridella | 4 | 677 | 140.62 |