hi,
I really like this paper for its idea.
and dislike it very much for its use of terms without explaining what they mean.

Considering the fact that NB classifiers are effective precisely because even though their probability estimates might be wrong their ranking are correct in many cases (since its easier to do discrimination than density estimation), maximizing conditional likelihood makes a lot of sense. I think this is an idea well worth exploring in other the context of learning algorithms also.

The results look impressive but the details on the algorithm are sketchy. I guess conference page limits do that to papers.


sameer