The importance of the depth for text-image selection strategy in Learning to Rank

Abstract : We examine the effect of the number documents being pooled, for constructing training sets, has on the performance of the learning-to-rank (LTR) approaches that use it to build our ranking functions. Our investigation takes place in a multimedia setting and uses the ImageCLEF photo 2006 dataset based on text and visual features. Experiments show that our LTR algorithm, OWPC,outperforms other baselines.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01284773
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Submitted on : Tuesday, March 8, 2016 - 10:18:31 AM
Last modification on : Thursday, March 21, 2019 - 1:05:16 PM

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David Buffoni, Sabrina Tollari, Patrick Gallinari. The importance of the depth for text-image selection strategy in Learning to Rank. European Conference on Information Retrieval (ECIR 2011), Apr 2011, Dublin, Ireland. Springer, European Conference on Information Retrieval (ECIR 2011), 6611, pp.743-746, Lecture Notes in Computer Science. 〈10.1007/978-3-642-20161-5_84〉. 〈hal-01284773〉

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