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Lower Bounds for the Empirical Minimization Algorithm

Mendelson, Shahar


In this correspondence, we present a simple argument that proves that under mild geometric assumptions on the class F and the set of target functions Τ, the empirical minimization algorithm cannot yield a uniform error rate that is faster than 1√k in t

CollectionsANU Research Publications
Date published: 2008
Type: Journal article
Source: IEEE Transactions on Information Theory
DOI: 10.1109/TIT.2008.926323


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