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			<subfield code="a">grangier:2006:ecml/IDIAP</subfield>
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		<datafield tag="245" ind1=" " ind2=" ">
			<subfield code="a">A Discriminative Approach for the Retrieval of Images from Text Queries</subfield>
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		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Grangier, David</subfield>
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		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Monay, Florent</subfield>
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		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Bengio, Samy</subfield>
		</datafield>
		<datafield tag="856" ind1="4" ind2="0">
			<subfield code="i">EXTERNAL</subfield>
			<subfield code="u">http://publications.idiap.ch/attachments/reports/2006/grangier_ecml06.pdf</subfield>
			<subfield code="x">PUBLIC</subfield>
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		<datafield tag="711" ind1="2" ind2=" ">
			<subfield code="a">European Conference on Machine Learning (ECML)</subfield>
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		<datafield tag="260" ind1=" " ind2=" ">
			<subfield code="c">2006</subfield>
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		<datafield tag="773" ind1=" " ind2=" ">
			<subfield code="c">162-173</subfield>
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		<datafield tag="520" ind1=" " ind2=" ">
			<subfield code="a">This work proposes a new approach to the retrieval of images from text queries. Contrasting with previous work, this method relies on a discriminative model: the parameters are selected in order to minimize a loss related to the ranking performance of the model, i.e. its ability to rank the relevant pictures above the non-relevant ones when given a text query. In order to minimize this loss, we introduce an adaptation of the recently proposed Passive-Aggressive algorithm. The generalization performance of this approach is then compared with alternative models over the Corel dataset. These experiments show that our method outperforms the current state-of-the-art approaches, e.g. the average precision over Corel test data is 21.6\% for our model versus 16.7\% for the best alternative, Probabilistic Latent Semantic Analysis.</subfield>
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