Linear Classification and Selective Sampling Under Low Noise Conditions

no code implementations • NeurIPS 2008 • Giovanni Cavallanti, Nicolò Cesa-Bianchi, Claudio Gentile

Using the so-called Tsybakov low noise condition to parametrize the instance distribution, we show bounds on the convergence rate to the Bayes risk of both the fully supervised and the selective sampling versions of the basic algorithm.

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