Edited by author 04-23-2001 12:42 PM
I also thing this is an elegant idea, though I'm not very familiar with error correcting codes. I like the fact that this approach is a unifying one, but it is not clear to me what ideas belong to the authors and what can be attributed to the original work by Dietterich & Bakiri. I believe this paper's additions are the use of loss functions.

I take issue with the paper's use of the term epsilon in Theorem 1 without defining it until 2 pages later, as far as I can see.

In their experiments, they first talk about using the AdaBoost learning algorithm, and using "decision stumps" as the base learner. What are decision stumps?