Modeling PU learning using probabilistic logic programming

The goal of learning from positive and unlabeled (PU) examples is to learn a classifier that predicts the posterior class probability. The challenge is that the available labels in the data are determined by (1) the true class, and (2) the labeling mechanism that selects which positive examples get...

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Veröffentlicht in:	Machine learning Jg. 113; H. 3; S. 1351 - 1372
Hauptverfasser:	Verreet, Victor, De Raedt, Luc, Bekker, Jessa
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.03.2024 Springer Nature B.V
Schlagworte:	Artificial Intelligence Classifiers Computer Science Control Empirical analysis Labeling Labels Learning Logic programming Machine Learning Mechatronics Modeling Modelling Natural Language Processing (NLP) Positive unlabeled learning Probabilistic logic programming Robotics Simulation and Modeling Special Issue on Learning and Reasoning 2022 Statistical analysis Unidentifiability Weak supervision Unidentifiability Weak supervision Positive unlabeled learning Probabilistic logic programming Modeling
ISSN:	0885-6125, 1573-0565, 1573-0565
Online-Zugang:	Volltext
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