sample_arxiv_response_utf8.xml

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    <responseDate>2017-12-07T10:34:37Z</responseDate>
    <request verb="GetRecord" identifier="oai:arXiv.org:1207.1019" metadataPrefix="oai_dc">http://export.arxiv.org/oai2</request>
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            <header>
               <identifier>oai:arXiv.org:1207.1019</identifier>
               <datestamp>2015-01-16</datestamp>
               <setSpec>cs</setSpec>
               <setSpec>stat</setSpec>
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               <oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
                   <dc:title>PAC-Bayesian Majority Vote for Late Classifier Fusion</dc:title>
                   <dc:creator>Morvant, Emilie</dc:creator>
                   <dc:creator>Habrard, Amaury</dc:creator>
                   <dc:creator>Ayache, Stéphane</dc:creator>
                   <dc:subject>Statistics - Machine Learning</dc:subject>
                   <dc:subject>Computer Science - Computer Vision and Pattern Recognition</dc:subject>
                   <dc:subject>Computer Science - Learning</dc:subject>
                   <dc:subject>Computer Science - Multimedia</dc:subject>
                   <dc:description>  A lot of attention has been devoted to multimedia indexing over the past few
                    years. In the literature, we often consider two kinds of fusion schemes: The
                    early fusion and the late fusion. In this paper we focus on late classifier
                    fusion, where one combines the scores of each modality at the decision level.
                    To tackle this problem, we investigate a recent and elegant well-founded
                    quadratic program named MinCq coming from the Machine Learning PAC-Bayes
                    theory. MinCq looks for the weighted combination, over a set of real-valued
                    functions seen as voters, leading to the lowest misclassification rate, while
                    making use of the voters' diversity. We provide evidence that this method is
                    naturally adapted to late fusion procedure. We propose an extension of MinCq by
                    adding an order- preserving pairwise loss for ranking, helping to improve Mean
                    Averaged Precision measure. We confirm the good behavior of the MinCq-based
                    fusion approaches with experiments on a real image benchmark.
                </dc:description>
                <dc:description>Comment: 7 pages, Research report</dc:description>
                <dc:date>2012-07-04</dc:date>
                <dc:type>text</dc:type>
                <dc:identifier>http://arxiv.org/abs/1207.1019</dc:identifier>
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