This has be asked before, but I still have not grasped it completely. I know that generative models model the feature distribution and that this includes modelling the P(x|y) and P(y), which are not required if we are trying to classify (find P(y|x)).
Question: Many text books say that it is easier to include features in discriminative models which is rarely explained. Also they mention that discriminative models allow overlapping features (features that are interdependent). Could anybody explain what this means and why is it true, or guide me to the place to read? I see that it is possible to include features in generative models as well, and I can't see why it should be easier or more efficient in discriminative models.