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Aarhus University
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A hypothesis selection algorithm achieves optimal sample complexity and a near-optimal approximation factor while improving the state-of-the-art runtime by reducing the dependency on failure probability from polynomial to logarithmic.
The $\gamma$-VC dimension, not just the standard VC dimension, dictates the sample complexity of boosting weak learners into strong predictors.