Optimal Aggregation of Classifiers in Statistical Learning
Classification can be considered as nonparametric estimation of sets, where the risk is defined by means of a specific distance between sets associated with misclassification error. It is shown that the rates of convergence of classifiers depend on two parameters: the complexity of the class of cand...
| Veröffentlicht in: | The Annals of Statistics. - Institute of Mathematical Statistics. - 32(2004), 1, Seite 135-166 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2004
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| Zugriff auf das übergeordnete Werk: | The Annals of Statistics |
| Schlagworte: | Classification Statistical Learning Aggregation of Classifiers Optimal Rates Empirical Processes Margin Complexity of Classes of Sets Philosophy Mathematics Behavioral sciences |
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