Revisiting Strategies for Fitting Logistic Regression for Positive and Unlabeled Data Cover

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Revisiting Strategies for Fitting Logistic Regression for Positive and Unlabeled Data

International Journal of Applied Mathematics and Computer Science

Volume 32 (2022): Issue 2 (June 2022)

By: Adam Wawrzeńczyk and Jan Mielniczuk

Open Access

|Jul 2022

Authors

Adam Wawrzeńczyk

a.wawrzenczyk@ipipan.waw.pl

Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland

Jan Mielniczuk

miel@ipipan.waw.pl

Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland

Faculty of Mathematics and Information Sciences, Warsaw University of Technology, Warsaw, Poland

DOI: https://doi.org/10.34768/amcs-2022-0022 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X

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Language: English

Page range: 299 - 309

Submitted on: Nov 5, 2021

Accepted on: Feb 10, 2022

Published on: Jul 4, 2022

Published by: University of Zielona Góra

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

positive and unlabeled learning,

empirical risk,

logistic regression,

concave-convex optimization

Related subjects:

Applied mathematics

© 2022 Adam Wawrzeńczyk, Jan Mielniczuk, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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