This paper investigates boosting naive Bayesian classiica-tion. It rst shows that boosting cannot improve the accuracy of the naive Bayesian classiier on average in a set of natural domains. By analyzing the reasons of boosting's failures, we propose to introduce tree structures into naive Bayesian classiication to improve the performance of boosting when working with naive Bayesian classiicati...

Modifying standard gradient boosting by replacing the embedded weak learner in favor of a strong(er) one, we present SyRBo: Symbolic-Regression Boosting. Experiments over 98 regression datasets show that adding small number stages -- between 2--5 to symbolic regressor, statistically significant improvements can often be attained. We note coding SyRBo on top any regressor is straightforward, and...

This is a survey of some theoretical results on boosting obtained from an analogous treatment of some regression and classi cation boosting algorithms. Some related papers include [J99] and [J00a,b,c,d], which is a set of (mutually overlapping) papers concerning the assumption of weak hypotheses, behavior of generalization error in the large time limit and during the process of boosting, compar...