Linear Classification and Selective Sampling Under Low Noise Conditions

NeurIPS 2008 Giovanni CavallantiNicolò Cesa-BianchiClaudio Gentile

We provide a new analysis of an efficient margin-based algorithm for selective sampling in classification problems. 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... (read more)

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