Name
Abstract | ||
|---|---|---|
In this letter, we target the problem of model selection for support vector classifiers through in-sample methods, which are particularly appealing in the small-sample regime. In particular, we describe the application of a trimmed hinge loss function to the Rademacher complexity and maximal discrepancy-based in-sample approaches and show that the selected classifiers outperform the ones obtained with other in-sample model selection techniques, which exploit a soft loss function, in classifying microarray data. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/s11063-012-9235-z | Neural Processing Letters |
Keywords | Field | DocType |
Support vector machine,Model selection,Rademacher complexity,Maximal discrepancy,Convex–concave programming | Hinge loss,Pattern recognition,Support vector machine,Rademacher complexity,Model selection,Exploit,Artificial intelligence,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
36 | 3 | 1370-4621 |
Citations | PageRank | References |
10 | 0.51 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Davide Anguita | 1 | 1001 | 70.58 |
| Alessandro Ghio | 2 | 667 | 35.71 |
| Luca Oneto | 3 | 830 | 63.22 |
| Sandro Ridella | 4 | 677 | 140.62 |