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EnglishIn 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 andmaximal 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.
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| Titolo: | In-Sample Model Selection for Trimmed Hinge Loss Support Vector Machine |
| Autori: | |
| Data di pubblicazione: | 2012 |
| Rivista: | |
| 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 andmaximal 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. |
| Handle: | http://hdl.handle.net/11567/522434 |
| Appare nelle tipologie: | 01.01 - Articolo su rivista |
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