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Local Rademacher Complexity: sharper risk bounds with and without unlabeled samples.

Identifieur interne : 000083 ( PubMed/Checkpoint ); précédent : 000082; suivant : 000084

Local 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.

RBID : pubmed:25734890

English descriptors

KwdEn :
- Artificial Intelligence, Models, Statistical.
MESH :
- Artificial Intelligence, Models, Statistical.

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

Affiliations:

Italie

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pubmed:25734890

Le document en format XML

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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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Local Rademacher Complexity: sharper risk bounds with and without unlabeled samples.

Local Rademacher Complexity: sharper risk bounds with and without unlabeled samples.

Source :

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

Pour générer des pages wiki