Optimal and Adaptive Algorithms for Online Boosting

@inproceedings{Beygelzimer2015OptimalAA,
  title={Optimal and Adaptive Algorithms for Online Boosting},
  author={Alina Beygelzimer and Satyen Kale and Haipeng Luo},
  booktitle={IJCAI},
  year={2015}
}
We study online boosting, the task of converting any weak online learner into a strong online learner. Based on a novel and natural definition of weak online learnability, we develop two online boosting algorithms. The first algorithm is an online version of boost-by-majority. By proving a matching lower bound, we show that this algorithm is essentially optimal in terms of the number of weak learners and the sample complexity needed to achieve a specified accuracy. The second algorithm is… CONTINUE READING
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