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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... (Update)
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BibTeX entry: (Update)
T. S. Jaakkola and D. Haussler. Exploiting generative models in discriminative classifiers. Preprint, Dept. of Computer Science, Univ. of California, available from http://www.cse.ucsc.edu/~haussler/pubs.html, 1998. http://citeseer.ist.psu.edu/jaakkola98exploiting.html More
@techreport{ jaakkola98exploiting,
author = "T. Jaakkola and D. Haussler",
title = "Exploiting generative models in discriminative classifiers",
institution = "Dept. of Computer Science, Univ. of California",
year = "1998",
url = "citeseer.ist.psu.edu/jaakkola98exploiting.html" }
Citations (may not include all citations):1291 The nature of statistical learning theory (context) - Vapnik - 1995
171 Hidden Markov models in computational biology: Applications .. - Krogh, Brown et al. - 1994
116 Scop: a structural classification of proteins database - Hubbard, Murzin et al. - 1997
107 Natural gradient works efficiently in learning - Amari - 1998
71 Hidden Markov models of biological primary sequence informat.. (context) - Baldi, Chauvin et al. - 1994
34 Multiple alignment using hidden Markov models (context) - Eddy - 1995
16 Predicting protein structure using hidden Markov models - Karplus, Sjolander et al. - 1997
5 Available from http://wol (context) - MacKay, gaussian - 1997
3 Probabilistic kernel methods (context) - Jaakkola, Haussler - 1998
The graph only includes citing articles where the year of publication is known.
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