Shedding Light on the Asymmetric Learning Capability of AdaBoost. (arXiv:1507.02084v1 [cs.LG] CROSS LISTED)

In this paper, we propose a different insight to analyze AdaBoost. This analysis reveals that, beyond some preconceptions, AdaBoost can be directly used as an asymmetric learning algorithm, preserving all its theoretical properties. A novel class-conditional description of AdaBoost, which models the actual asymmetric behavior of the algorithm, is presented.

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