Exploiting Generative Models in Discriminative Classifiers

Generative probability models such as hidden ~larkov models pro(cid: 173) vide a principled way of treating missing information and dealing with variable length sequences. On the other hand, discriminative methods such as support vector machines enable us to construct flexible decision boundaries and often result in classification per(cid: 173) formance superior to 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 combina(cid: 173) tion by deriving kernel functions for use in discriminative methods such as support vector machines from generative probability mod(cid: 173) els. We provide a theoretical justification for this combination as well as demonstrate a substantial improvement in the classification performance in the context of D~A and protein sequence analysis.

Authors

• Tommi Jaakkola
• David Haussler

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