MadaBoost: A Modification of AdaBoost
نویسندگان
چکیده
We propse 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 prove that one version of MadaBoost is in fact a boosting algorithm, and we show how our algorithm can be used in detail. We then prove 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].
منابع مشابه
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, AdaBoos...
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In this paper we present an empirical comparison of algorithm AdaBoost with its modification called MadaBoost suitable for the boosting by filtering framework. In the boosting by filtering one obtains an unweighted sample at each stage that is randomly drawn from the current modified distribution in contrast with the boosting by subsampling where one uses a weighted sample at each stage. A boos...
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