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Learning from Corrupted Binary Labels via Class-Probability Estimation

Menon, Aditya; Van Rooyen, Brendan; Ong, Cheng Song; Williamson, Robert

Description

Many supervised learning problems involve learning from samples whose labels are corrupted in some way. For example, each sample may have some constant probability of being incorrectly labelled (learning with label noise), or one may have a pool of unlabelled samples in lieu of negative samples (learning from positive and unlabelled data). This paper uses class-probability estimation to study these and other corruption processes belonging to the mutually contaminated distributions framework...[Show more]

CollectionsANU Research Publications
Date published: 2015
Type: Conference paper
URI: http://hdl.handle.net/1885/103788

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