Improvement of Boosting Algorithm by Modifying the Weighting Rule

@article{Nakamura2002ImprovementOB,
  title={Improvement of Boosting Algorithm by Modifying the Weighting Rule},
  author={Masayuki Nakamura and Hiroki Nomiya and Kuniaki Uehara},
  journal={Annals of Mathematics and Artificial Intelligence},
  year={2002},
  volume={41},
  pages={95-109}
}
AdaBoost is a method for improving the classification accuracy of a given learning algorithm by combining hypotheses created by the learning alogorithms. One of the drawbacks of AdaBoost is that it worsens its performance when training examples include noisy examples or exceptional examples, which are called hard examples. The phenomenon causes that AdaBoost assigns too high weights to hard examples. In this research, we introduce the thresholds into the weighting rule of AdaBoost in order to… CONTINUE READING
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