Revisiting strategies for fitting logistic regression for positive and unlabeled data

DOI10.34768/amcs-2022-0022OpenAlexW4385059892MaRDI QIDQ2162143

Adam Wawrzeńczyk, Jan Mielniczuk

Publication date: 5 August 2022

Published in: International Journal of Applied Mathematics and Computer Science (Search for Journal in Brave)

Full work available at URL: https://doaj.org/article/2b7541d84b6449a9ae55644459c31d7d

zbMATH Keywords

logistic regressionempirical riskpositive and unlabeled learningconcave-convex optimization

Mathematics Subject Classification ID

Statistical sampling theory and related topics (62Dxx)

Uses Software

• DCCP

Cites Work

• Unnamed Item
• Learning from positive and unlabeled data: a survey
• Variations and extension of the convex-concave procedure
• Dealing with under-reported variables: an information theoretic solution
• Estimating the class prior for positive and unlabelled data via logistic regression
• Presence‐Only Data and the EM Algorithm
• The Concave-Convex Procedure

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