Discriminative Learning can Succeed where Generative Learning Fails
Phil Long, Rocco Servedio and Hans Simon
Information Processing Letters
Generative algorithms for learning classifiers use training data to
separately estimate a probability model for each class. New items
are 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. This statement
is formalized using a framework inspired by previous work of Goldberg.