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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.
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