Exploiting Generative Models in Discriminative Classifiers

On the other hand, discriminative methods such as support vector machines enable us to construct flexible decision boundaries and often result in classification performance superiorto that of the model based approaches. An ideal classifier should combine these two complementary approaches. In this paper, we develop a natural way of achieving this combination byderiving kernel functions for use in discriminative methods such as support vector machines from generative probability models.