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Rademacher Observations, Private Data, and Boosting

Nock, Richard; Patrini, Giorgio; Friedman, Arik


The minimization of the logistic loss is a popular approach to batch supervised learning. Our paper starts from the surprising observation that, when fitting linear (or kernelized) classifiers, the minimization of the logistic loss is \textit{equivalent} to the minimization of an exponential \textit{rado}-loss computed (i) over transformed data that we call Rademacher observations (rados), and (ii) over the \textit{same} classifier as the one of the logistic loss. Thus, a classifier learnt from...[Show more]

CollectionsANU Research Publications
Date published: 2015
Type: Conference paper


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