Positive-Unlabeled Learning With Label Distribution Alignment
Positive-Unlabeled (PU) data arise frequently in a wide range of fields such as medical diagnosis, anomaly analysis and personalized advertising. The absence of any known negative labels makes it very challenging to learn binary classifiers from such data. Many state-of-the-art methods reformulate t...
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Détails bibliographiques
| Publié dans: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 45(2023), 12 vom: 05. Dez., Seite 15345-15363
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| Auteur principal: |
Jiang, Yangbangyan
(Auteur) |
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| Autres auteurs: |
Xu, Qianqian,
Zhao, Yunrui,
Yang, Zhiyong,
Wen, Peisong,
Cao, Xiaochun,
Huang, Qingming |
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| Format: | Article en ligne
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| Langue: | English |
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| Publié: |
2023
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| Accès à la collection: | IEEE transactions on pattern analysis and machine intelligence
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| Sujets: | Journal Article |
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