Date(s) : 12/06/2015 iCal
14 h 00 min - 15 h 00 min
Title: Guaranteed risk for large margin multi-category classifiers\n \nAbstract: In the framework of agnostic learning\, the two basic parameters of a multi-class discrimination problem are the sample size m and the number of categories C. In 2007\, we contributed to the Vapnik-Chervonenkis theory of large margin multi-category classifiers by introducing the appropriate class of generalized Vapnik-Chervonenkis dimensions: the class of gamma-psi-dimensions. The guaranteed risk we derived exhibited a suboptimal ln(m) / m^1/2 convergence rate. In 2012\, Mohri and his co-authors obtained the optimal 1/m^1/2 rate\, with a control term growing quadratically with C. In this talk\, we prove that this result can be improved upon\, by establishing a bound of equal convergence rate and better dependency on C\, namely a O(C^3/2).
Catégories Pas de Catégories