Exploiting Generative Models in Discriminative Classifiers

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Exploiting Generative Models in Discriminative Classifiers (1998)

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by Tommi Jaakkola , David Haussler

Venue:	In Advances in Neural Information Processing Systems 11
Citations:	551 - 9 self

BibTeX

@INPROCEEDINGS{Jaakkola98exploitinggenerative,
    author = {Tommi Jaakkola and David Haussler},
    title = {Exploiting Generative Models in Discriminative Classifiers},
    booktitle = {In Advances in Neural Information Processing Systems 11},
    year = {1998},
    pages = {487--493},
    publisher = {MIT Press}
}

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Abstract

Generative probability models such as hidden Markov models provide 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 performance 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 combination by deriving kernel functions for use in discriminative methods such as support vector machines from generative probability models. We provide a theoretical justification for this combination as well as demonstrate a substantial improvement in the classification performance in the context of DNA and protein sequence analysis.

Keyphrases

generative model discriminative classifier discriminative method generative probability model classification performance support vector machine ideal classifier protein sequence analysis kernel function principled way variable length sequence hidden markov model flexible decision boundary complementary approach substantial improvement natural way theoretical justification

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