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<title lang=en>Boosting Nearest Neighbors for the Efficient Estimation of Posteriors</title>
<creator>D'Ambrosio, Roberto</creator>
<creator>Nock, Richard</creator>
<creator>Bel Haj Ali, Wafa</creator>
<creator>Nielsen, Frank</creator>
<creator>Barlaud, Michel</creator>
<contributor>Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe IMAGES-CREATIVE ; 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 Laboratory Paris (SONY CSL-Paris) ; Sony</contributor>
<description>International audience</description>
<source>European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)</source>
<source>ECML-PKDD 2012</source>
<coverage>Bristol, United Kingdom</coverage>
<identifier>hal-00702771</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00702771</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00702771/document</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00702771/file/ecml12-dnbnb-sub.pdf</identifier>
<source>https://hal.archives-ouvertes.fr/hal-00702771</source>
<source>ECML-PKDD 2012, Sep 2012, Bristol, United Kingdom. pp.16, 2012</source>
<language>en</language>
<subject lang=en>classification</subject>
<subject lang=en>boosting</subject>
<subject lang=en>kNN</subject>
<subject lang=en>estimation of posteriors</subject>
<subject lang=en>Machine learning</subject>
<subject>springer</subject>
<subject>[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]</subject>
<subject>[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>It is an admitted fact that mainstream boosting algorithms like AdaBoost do not perform well to estimate class conditional probabilities. In this paper, we analyze, in the light of this problem, a recent algorithm, unn, which leverages nearest neighbors while minimizing a convex loss. Our contribution is threefold. First, we show that there exists a subclass of surrogate losses, elsewhere called balanced, whose minimization brings simple and statistically efficient estimators for Bayes posteriors. Second, we show explicit convergence rates towards these estimators for unn, for any such surrogate loss, under a Weak Learning Assumption which parallels that of classical boosting results. Third and last, we provide experiments and comparisons on synthetic and real datasets, including the challenging SUN computer vision database. Results clearly display that boosting nearest neighbors may provide highly accurate estimators, sometimes more than a hundred times more accurate than those of other contenders like support vector machines.</description>
<date>2012-09-24</date>
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