Binary classification with corrupted labels

DOI10.1214/22-EJS1987zbMath1493.62390arXiv2106.09136OpenAlexW4214943072MaRDI QIDQ2136645

Rina Foygel Barber, Yonghoon Lee

Publication date: 11 May 2022

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2106.09136

zbMATH Keywords

classificationlabel noise

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Cites Work

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• Classification with asymmetric label noise: consistency and maximal denoising
• Oracle inequalities in empirical risk minimization and sparse recovery problems. École d'Été de Probabilités de Saint-Flour XXXVIII-2008.
• Learning from binary labels with instance-dependent noise
• Learning kernel logistic regression in the presence of class label noise
• Random classification noise defeats all convex potential boosters
• Adaptive transfer learning
• Classification with imperfect training labels

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