Abstract

Generative kernels, a generalised form of Fisher kernels, are a powerful form of kernel that allow the kernel parameters to be tuned to a specific task. The standard approach to training these kernels is to use maximum likelihood estimation. This paper describes a novel approach based on maximum-margin training of both the kernel parameters and a Support Vector Machine (SVM) classifier. It combines standard SVM training with a gradient-descent based kernel parameter optimisation scheme. This allows the kernel parameters to be explicitly trained for the data set and the SVM score-space. Initial results on an artificial task and the Deterding data show that such an approach can reduce classification error rates. 1 1

Citations