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Merge pull request #51 from rero/rero-encoding-support
feat: add encoding support
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<?xml version="1.0" encoding="UTF-8"?> | ||
<OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"> | ||
<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> | ||
<GetRecord> | ||
<record> | ||
<header> | ||
<identifier>oai:arXiv.org:1207.1019</identifier> | ||
<datestamp>2015-01-16</datestamp> | ||
<setSpec>cs</setSpec> | ||
<setSpec>stat</setSpec> | ||
</header> | ||
<metadata> | ||
<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> | ||
</oai_dc:dc> | ||
</metadata> | ||
</record> | ||
</GetRecord> | ||
</OAI-PMH> |
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