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Optimal and Adaptive Algorithms for Online Boosting
[article]
2015
arXiv
pre-print
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. This optimal algorithm is not
arXiv:1502.02651v1
fatcat:svaj4rrgxfgfxbbzta54l5vuq4