Generative or Discriminative? Getting the Best of Both Worlds
Chris Bishop, Julia Lasserre · 2007
Abstract When labelled training data is plentiful, discriminative techniques are widely used since they give excellent generalization performance. However, although collection of data is often easy, the process of labelling it can be expensive. Consequently there is increasing interest in generative methods since these can exploit unlabelled data in addition to labelled data. Although the generalization performance of generative models can often be improved by ‘training them discriminatively’, they can then no longer make use of unlabelled data. In an attempt to gain the benefit of both generative and discriminative approaches, heuristic procedure have been proposed which interpolate between these two extremes by taking a convex combination of the generative and discriminative objective functions.