SVMs, Gaussian mixtures, and their generative/discriminative fusion

Abstract

We present a new technique that employs support vector machines and Gaussian mixture densities to create a generative/discriminative joint classifier. In the past, several approaches to fuse the advantages of generative and discriminative approaches were presented, often leading to improved robustness and recognition accuracy. The presented method directly fuses both approaches, effectively allowing to fully exploit the advantages of both. The fusion of SVMs and GMDs is done by representing SVMs in the framework of GMDs without changing the training and without changing the decision boundary. The new classifier is evaluated on four tasks from the UCI machine learning repository. It is shown that for the relatively rare cases where SVMs have problems, the combined method outperforms both individual ones.