18 . 657 : Mathematics of Machine Learning
نویسنده
چکیده
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 which has been studied last time. Especially, we achieved the fast rate logM n in the noiseless case by assuming h ∈ H which implies that h̄ = h. This assumption was essential for the proof and we will see why it is necessary again in the following section.
منابع مشابه
18 . 657 : Mathematics of Machine Learning
Recall form last lectures that in prediction with expert advise, at each time t, the player plays at ∈ {e1, . . . , ek} and the adversary plays zt such that l(at, zt) ≤ 1 for some loss function. One example of such loss function is linear function l(at, zt) = a T t zt where |zt|∞ ≤ 1. Linear bandits are a more general setting where the player selects an action at ∈ A ⊂ Rk, where A is a convex s...
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