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<OAI-PMH schemaLocation=http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd> <responseDate>2018-01-15T18:31:32Z</responseDate> <request identifier=oai:HAL:hal-00959125v1 verb=GetRecord metadataPrefix=oai_dc>http://api.archives-ouvertes.fr/oai/hal/</request> <GetRecord> <record> <header> <identifier>oai:HAL:hal-00959125v1</identifier> <datestamp>2018-01-11</datestamp> <setSpec>type:COMM</setSpec> <setSpec>subject:info</setSpec> <setSpec>collection:CNRS</setSpec> <setSpec>collection:UNICE</setSpec> <setSpec>collection:I3S</setSpec> <setSpec>collection:UNIV-AG</setSpec> <setSpec>collection:BNRMI</setSpec> <setSpec>collection:CEREGMIA</setSpec> <setSpec>collection:UCA-TEST</setSpec> <setSpec>collection:UNIV-COTEDAZUR</setSpec> </header> <metadata><dc> <publisher>HAL CCSD</publisher> <title lang=en>Fast Newton Nearest Neighbors Boosting For Image Classification</title> <creator>Bel Haj Ali, Wafa</creator> <creator>Nock, Richard</creator> <creator>Nielsen, Franck</creator> <creator>Barlaud, Michel</creator> <contributor>Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Projet MEDIACODING ; Signal, Images et Systèmes (SIS) ; Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S) ; Université Nice Sophia Antipolis (UNS) ; Université Côte d'Azur (UCA) - Université Côte d'Azur (UCA) - Centre National de la Recherche Scientifique (CNRS) - Université Nice Sophia Antipolis (UNS) ; Université Côte d'Azur (UCA) - Université Côte d'Azur (UCA) - Centre National de la Recherche Scientifique (CNRS) - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S) ; Université Nice Sophia Antipolis (UNS) ; Université Côte d'Azur (UCA) - Université Côte d'Azur (UCA) - Centre National de la Recherche Scientifique (CNRS) - Université Nice Sophia Antipolis (UNS) ; Université Côte d'Azur (UCA) - Université Côte d'Azur (UCA) - Centre National de la Recherche Scientifique (CNRS)</contributor> <contributor>Centre de Recherche en Economie, Gestion, Modélisation et Informatique Appliquée (CEREGMIA) ; Université des Antilles et de la Guyane (UAG)</contributor> <contributor>Sony Computer Science Laboratories [Tokyo, Japan] ; Sony</contributor> <description>International audience</description> <source>IEEE International Workshop on Machine Learning for Signal Processing</source> <source>MLSP - 23rd Workshop on Machine Learning for Signal Processing</source> <coverage>Southampton, United Kingdom</coverage> <publisher>IEEE</publisher> <identifier>hal-00959125</identifier> <identifier>https://hal.archives-ouvertes.fr/hal-00959125</identifier> <identifier>https://hal.archives-ouvertes.fr/hal-00959125/document</identifier> <identifier>https://hal.archives-ouvertes.fr/hal-00959125/file/BNNB_mlsp2013.pdf</identifier> <source>https://hal.archives-ouvertes.fr/hal-00959125</source> <source>MLSP - 23rd Workshop on Machine Learning for Signal Processing, Sep 2013, Southampton, United Kingdom. IEEE, pp.6, 2013</source> <language>en</language> <subject lang=en>Machine learning</subject> <subject>[INFO.INFO-TI] Computer Science [cs]/Image Processing</subject> <subject>[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]</subject> <type>info:eu-repo/semantics/conferenceObject</type> <type>Conference papers</type> <description lang=en>Recent works display that large scale image classification problems rule out computationally demanding methods. On such problems, simple approaches like k-NN are affordable contenders, with still room space for statistical improvements under the algorithmic constraints. A recent work showed how to leverage k-NN to yield a formal boosting algorithm. This method, however, has numerical issues that make it not suited for large scale problems. We propose here an Adaptive Newton-Raphson scheme to leverage k-NN, N3, which does not suffer these issues. We show that it is a boosting algorithm, with several key algorithmic and statistical properties. In particular, it may be sufficient to boost a subsample to reach desired bounds for the loss at hand in the boosting framework. Experiments are provided on the SUN, and Caltech databases. They confirm that boosting a subsample -- sometimes containing few examples only -- is sufficient to reach the convergence regime of N3. Under such conditions, N3 challenges the accuracy of contenders with lower computational cost and lower memory requirement.</description> <date>2013-09-22</date> </dc> </metadata> </record> </GetRecord> </OAI-PMH>