Discriminative Learning Can Succeed Where Generative Learning Fails
Abstract
Generative algorithms for learning classifiers use training data to separately estimate a probability model for each class. New items are then classified by comparing their probabilities under these models. In contrast, discriminative learning algorithms try to find classifiers that perform well on all the training data. We show that there is a learning problem that can be solved by a discriminative learning algorithm, but not by any generative learning algorithm (given minimal cryptographic assumptions). This statement is formalized using a framework inspired by previous work of Goldberg [3].
Cite
Text
Long and Servedio. "Discriminative Learning Can Succeed Where Generative Learning Fails." Annual Conference on Computational Learning Theory, 2006. doi:10.1007/11776420_25Markdown
[Long and Servedio. "Discriminative Learning Can Succeed Where Generative Learning Fails." Annual Conference on Computational Learning Theory, 2006.](https://mlanthology.org/colt/2006/long2006colt-discriminative/) doi:10.1007/11776420_25BibTeX
@inproceedings{long2006colt-discriminative,
title = {{Discriminative Learning Can Succeed Where Generative Learning Fails}},
author = {Long, Philip M. and Servedio, Rocco A.},
booktitle = {Annual Conference on Computational Learning Theory},
year = {2006},
pages = {319-334},
doi = {10.1007/11776420_25},
url = {https://mlanthology.org/colt/2006/long2006colt-discriminative/}
}