Noise-adaptive Margin-based Active Learning and Lower Bounds under Tsybakov Noise Condition

20 Jun 2014 Yining Wang Aarti Singh

We present a simple noise-robust margin-based active learning algorithm to find homogeneous (passing the origin) linear separators and analyze its error convergence when labels are corrupted by noise. We show that when the imposed noise satisfies the Tsybakov low noise condition (Mammen, Tsybakov, and others 1999; Tsybakov 2004) the algorithm is able to adapt to unknown level of noise and achieves optimal statistical rate up to poly-logarithmic factors... (read more)

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