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<datestamp>2018-01-11</datestamp>
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<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 <i>k</i>-NN are affordable contenders, with still room space for statistical improvements under the algorithmic constraints. A recent work showed how to leverage <i>k</i>-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 <i>k</i>-NN, N<sup>3</sup>, 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 N<sup>3</sup>. Under such conditions, N<sup>3</sup> challenges the accuracy of contenders with lower computational cost and lower memory requirement.</description>
<date>2013-09-22</date>
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