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UID:2246@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20180319T153000
DTEND;TZID=Europe/Paris:20180319T163000
DTSTAMP:20180304T143000Z
URL:https://www.i2m.univ-amu.fr/evenements/hyperbolic-m-svm-a-generalizati
 on-of-svm/
SUMMARY: (...): Hyperbolic M-SVM: a generalization of SVM
DESCRIPTION:: In machine learning\, support vector machines (SVMs) are supe
 rvised learning models with associated learning algorithms that analyze da
 ta used for classification and regression analysis.  In this talk\, I will
  introduce a new margin multi category  classifier based on classes of vec
 tor valued functions with one component function per category\, it is a ke
 rnel machine whose separation surfaces are hyperbolic and generalizes the 
 SVMs. I will also exhibit the statistical properties of this classifier \,
  I will show that the classes of component functions are uniform Glivenko-
 Cantelli (GC).  I will  then  found the guaranteed risk  of this classifie
 r.Finally\, I will  exhibit a margin loss function ensuring the Fisher con
 sistency.http://math.univ-lille1.fr/~dakdouki/
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DTSTART:20171029T020000
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