Download | - View accepted manuscript: A self–training method for learning to rank with with unlabeled data (PDF, 554 KiB)
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Author | Search for: Truong, Tuong Vinh; Search for: Amini, Massih-Reza1; Search for: Gallinari, Patrick |
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Affiliation | - National Research Council of Canada. NRC Institute for Information Technology
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Format | Text, Article |
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Conference | The 11th European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, April 22-24, 2009 |
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Abstract | 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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Publication date | 2009 |
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In | |
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Language | English |
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Peer reviewed | Yes |
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NPARC number | 16435916 |
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Export citation | Export as RIS |
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Report a correction | Report a correction (opens in a new tab) |
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Record identifier | 1f4e0827-4cb1-4865-8e6f-1e87715f8d23 |
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Record created | 2010-11-24 |
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Record modified | 2020-04-16 |
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