Exploiting Generative Models in Discriminative Classifiers
Abstract
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.
Cite
Text
Jaakkola and Haussler. "Exploiting Generative Models in Discriminative Classifiers." Neural Information Processing Systems, 1998.Markdown
[Jaakkola and Haussler. "Exploiting Generative Models in Discriminative Classifiers." Neural Information Processing Systems, 1998.](https://mlanthology.org/neurips/1998/jaakkola1998neurips-exploiting/)BibTeX
@inproceedings{jaakkola1998neurips-exploiting,
title = {{Exploiting Generative Models in Discriminative Classifiers}},
author = {Jaakkola, Tommi and Haussler, David},
booktitle = {Neural Information Processing Systems},
year = {1998},
pages = {487-493},
url = {https://mlanthology.org/neurips/1998/jaakkola1998neurips-exploiting/}
}