Knowledge and Data Management White Papers
Principled Hybrids of Generative and Discriminative Models
Overview When labelled training data is plentiful, discriminative techniques are widely used since they give excellent generalization performance. However, for large-scale applications such as object recognition, hand labelling of data is expensive, and there is much interest in semi-supervised techniques based on generative models in which the majority of the training data is unlabelled. 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. This paper adopts a new perspective which says that there is only one correct way to train a given model, and that a 'Discriminatively trained' generative model is fundamentally a new model.
| Publisher | Microsoft | File Format | |
|---|---|---|---|
| Date Published | September 2006 | ||
| Format | White Papers | ||
| Topics | |||
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