Rademacher complexity properties 2: finite classes and margin losses
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چکیده
To make this meaningful to machine learning, we need to replace Ef with some form of risk. Today will discuss three choices. 1. R` where ` is Lipschitz. We covered this last time but will recap a little. 2. Rz(f) := Pr[f(X) 6= Y ]; for this we’ll use finite classes and discuss shatter coefficients and VC dimension. 3. Rγ(f) = R`γ where `γ(z) := max{0,min{z/γ+ 1, 1}} will lead to nice bounds for a number of methods, for instance boosting.
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تاریخ انتشار 2016