| Téléchargement | - Voir le manuscrit accepté : A self–training method for learning to rank with with unlabeled data (PDF, 554 Kio)
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| Auteur | Rechercher : Truong, Tuong Vinh; Rechercher : Amini, Massih-Reza1; Rechercher : Gallinari, Patrick |
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| Affiliation | - Conseil national de recherches du Canada. Institut de technologie de l'information du CNRC
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| Format | Texte, Article |
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| Conférence | The 11th European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, April 22-24, 2009 |
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| Résumé | This paper presents a new algorithm for bipartite ranking functions trained with partially labeled data. The algorithm is an extension of the self–training paradigm developed under the classification frame- work. We further propose an efficient and scalable optimization method for training linear models though the approach is general in the sense that it can be applied to any classes of scoring functions. Empirical results on several common image and text corpora over the Area Under the ROC Curve (AUC) and the Average Precision measure show that the use of unlabeled data in the training process leads to improve the performance of baseline supervised ranking functions. |
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| Date de publication | 2009 |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Numéro NPARC | 16435916 |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 1f4e0827-4cb1-4865-8e6f-1e87715f8d23 |
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| Enregistrement créé | 2010-11-24 |
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| Enregistrement modifié | 2020-04-16 |
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