Skip navigation
Skip navigation

Learning from Corrupted Binary Labels via Class-Probability Estimation

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


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


File Description SizeFormat Image
01_Menon_Learning_from_Corrupted_Binary_2015.pdf342.01 kBAdobe PDF    Request a copy

Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  19 May 2020/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator