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000029587 0248_ $$aoai:doc.rero.ch:20120713104212-OD$$ppostprint$$prero_explore$$zthesis_urn$$zreport$$zthesis$$zcdu004$$zbook$$zjournal$$zcdu16$$zhegge$$zpreprint$$zcdu1$$zdissertation$$zcdu34
000029587 041__ $$aeng
000029587 080__ $$a004
000029587 100__ $$aDo, Huyen$$uCUI, Université de Genève
000029587 245__ $$9eng$$aA metric learning perspective of SVM$$bon the relation of LMNN and SVM
000029587 269__ $$c2012
000029587 520__ $$9eng$$aSupport Vector Machines, SVMs, and the Large Margin Nearest Neighbor algorithm, LMNN, are two very popular learning algorithms with quite different learning biases. In this paper we bring them into a unified view and show that they have a much stronger relation than what is commonly thought. We analyze SVMs from a metric learning perspective and cast them as a metric learning problem, a view which helps us uncover the relations of the two algorithms. We show that LMNN can be seen as learning a set of local SVM-like models in a quadratic space. Along the way and inspired by the metric-based interpretation of SVMs we derive a novel variant of SVMs, 
000029587 695__ $$9eng$$aSupport Vector Machines ; Large Margin Nearest Neighbor ; metric learning perspective
000029587 700__ $$aKalousis, Alexandros$$uHaute école de gestion de Genève
000029587 700__ $$aWang, Jun$$uCUI, Université de Genève
000029587 700__ $$aWoznica, Adam$$uCUI, Université de Genève
000029587 773__ $$g2012/22//308-317$$tProceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS) April 21-23, 2012 La Palma, Canary Islands
000029587 775__ $$gAISTATS 2012$$ohttp://jmlr.csail.mit.edu/proceedings/papers/v22/do12/do12.pdf
000029587 8564_ $$fKalousis_2012_Metric_learning_perspective_of_svm.pdf$$qapplication/pdf$$s610799$$uhttp://doc.rero.ch/record/29587/files/Kalousis_2012_Metric_learning_perspective_of_svm.pdf$$yorder:1$$zTexte intégral
000029587 918__ $$aHaute école de gestion de Genève$$bCampus de Battelle, Bâtiment F, 7 route de Drize, 1227 Carouge$$cCentre de recherche appliqué en gestion (CRAG)
000029587 919__ $$aHaute école de gestion de Genève$$bGenève$$ddoc.support@rero.ch
000029587 980__ $$aPOSTPRINT$$bHEGGE$$fART_INPROC
000029587 990__ $$a20120713104212-OD