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.
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.
and thus,
it is robust to random classification noise.
(This is a revised version of TR-C133.)