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ICANN
2005
Springer
15 years 3 months ago
Training of Support Vector Machines with Mahalanobis Kernels
Abstract. Radial basis function (RBF) kernels are widely used for support vector machines. But for model selection, we need to optimize the kernel parameter and the margin paramete...
Shigeo Abe
COLT
2003
Springer
15 years 2 months ago
Learning with Rigorous Support Vector Machines
We examine the so-called rigorous support vector machine (RSVM) approach proposed by Vapnik (1998). The formulation of RSVM is derived by explicitly implementing the structural ris...
Jinbo Bi, Vladimir Vapnik
DAM
2008
83views more  DAM 2008»
14 years 9 months ago
Multi-group support vector machines with measurement costs: A biobjective approach
Support Vector Machine has shown to have good performance in many practical classification settings. In this paper we propose, for multi-group classification, a biobjective optimi...
Emilio Carrizosa, Belen Martin-Barragan, Dolores R...
ICML
2007
IEEE
15 years 10 months ago
Solving multiclass support vector machines with LaRank
Optimization algorithms for large margin multiclass recognizers are often too costly to handle ambitious problems with structured outputs and exponential numbers of classes. Optim...
Antoine Bordes, Jason Weston, Léon Bottou, ...
NECO
2007
107views more  NECO 2007»
14 years 9 months ago
Training a Support Vector Machine in the Primal
Most literature on Support Vector Machines (SVMs) concentrate on the dual optimization problem. In this paper, we would like to point out that the primal problem can also be solve...
Olivier Chapelle