on Mathematical and Computing Sciences : TR - C 138 title : MadaBoost : A modification of

In the last decade, one of the research topics that has received a great deal of attention from the machine learning and computational learning communities has been the so called boosting techniques. In this paper, we further explore this topic by proposing a new boosting algorithm that mends some of the problems that have been detected in the, so far most successful boosting algorithm, AdaBoost due to Freund and Schapire [FS97]. These problems are: (1) AdaBoost cannot be used in the boosting by filtering framework, and (2) AdaBoost does not seem to be noise resistant. In order to solve them, we propose a new boosting algorithm MadaBoost by modifying the weighting system of AdaBoost. We first prove that one version of MadaBoost is in fact a boosting algorithm. Second, we show how our algorithm can be used and analyzed its performance in detail. Finally, we show that our new boosting algorithm can be casted in the statistical query learning model [Kea93] and thus, it is robust to random classification noise [AL88]. (This is a revised version of TR-C133.)