Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel

Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel

Support vector machines (SVMs) with the gaussian (RBF) kernel have been popular for practical use. Model selection in this class of SVMs involves two hyperparameters: the penalty parameter C and the kernel width sigma. This letter analyzes the behavior of the SVM classifier when these hyperparameters take very small or very large values. Our results help in understanding the hyperparameter spac...

Asymptotic Behaviors Of Support Vector Machines . . . .

Fast training of Support Vector Machines with Gaussian kernel

Support Vector Machines (SVM’s) are ubiquitous and attracted a huge interest in the last years. Their training involves the definition of a suitable optimization model with two main features: (1) its optimal solution estimates the a-posteriori optimal SVM parameters in a reliable way, and (2) it can be solved efficiently. Hinge-loss models, among others, have been used with remarkable success t...

Proposed Gaussian Kernel Support Vector Machines for Regression

Asymptotic Behaviour of Support Vector Machines

A better submitted version of this paper is available at http://eric.univ-lyon2.fr/~ oteytaud. This report only completes the paper for some results of lower interest and we suggest that interested readers rst read the submitted version and only completes this reading by this paper if they want more information on some results not detailed in the submitted version. This report was rst written i...