In this paper, we derive a novel probabilistic model of boosting as a Product
of Experts. We re-derive the boosting algorithm as a greedy incremental model
selection procedure which ensures that addition of new experts to the ensemble
does not decrease the likelihood of the data. These learning rules lead to a
generic boosting algorithm - POE- Boost which turns out to be similar to the
AdaBoost algorithm under certain assumptions on the expert probabilities. The
paper then extends the POEBoost algorithm to POEBoost.CS which handles
hypothesis that produce probabilistic predictions. This new algorithm is shown
to have better generalization performance compared to other state of the art
algorithms
