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- W3171565825 abstract "We present a series of new and more favorable margin-based learning guarantees that depend on the empirical margin loss of a predictor. We give two types of learning bounds, both data-dependent ones and bounds valid for general families, in terms of the Rademacher complexity or the empirical $ell_infty$ covering number of the hypothesis set used. We also briefly highlight several applications of these bounds and discuss their connection with existing results." @default.
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- W3171565825 date "2021-07-18" @default.
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- W3171565825 title "Relative Deviation Margin Bounds" @default.
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