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Learning with Symmetric Label Noise: The Importance of Being Unhinged

Van Rooyen, Brendan; Menon, Aditya; Williamson, Robert


Convex potential minimisation is the de facto approach to binary classification. However, Long and Servedio [2008] proved that under symmetric label noise (SLN), minimisation of any convex potential over a linear function class can result in classification performance equivalent to random guessing. This ostensibly shows that convex losses are not SLN-robust. In this paper, we propose a convex, classification-calibrated loss and prove that it is SLN-robust. The loss avoids the Long and Servedio...[Show more]

CollectionsANU Research Publications
Date published: 2015
Type: Conference paper
Source: Reflection, Refraction and Hamiltonian Monte Carlo
Access Rights: Open Access


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