In this lecture, we continue to discuss the effect of noise on the rate of the excess risk E(ĥ) = R(ĥ) − R(h) where ĥ is the empirical risk minimizer. In the binary classification model, noise roughly means how close the regression function η is from 1 2 . In particular, if η = 1 2 then we observe only noise, and if η ∈ {0, 1} we are in the noiseless case… (More)

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