Local Rademacher Complexity: sharper risk bounds with and without unlabeled samples.
Identifieur interne : 000083 ( PubMed/Checkpoint ); précédent : 000082; suivant : 000084Local Rademacher Complexity: sharper risk bounds with and without unlabeled samples.
Auteurs : Luca Oneto [Italie] ; Alessandro Ghio [Italie] ; Sandro Ridella [Italie] ; Davide Anguita [Italie]Source :
- Neural networks : the official journal of the International Neural Network Society ; 2015.
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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.
DOI: 10.1016/j.neunet.2015.02.006
PubMed: 25734890
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<front><div type="abstract" xml:lang="en">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.</div>
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