Learning from positive and unlabeled examples

DOI10.1016/j.tcs.2005.09.007zbMath1081.68081OpenAlexW2130652100MaRDI QIDQ2581364

François Denis, Fabien Letouzey, Rémi Gilleron

Publication date: 10 January 2006

Published in: Theoretical Computer Science (Search for Journal in Brave)

Full work available at URL: https://hal.inria.fr/inria-00536692/file/onlypos02.pdf

zbMATH Keywords

Data miningPAC learningSemi-supervised learningStatistical query model

Mathematics Subject Classification ID

Database theory (68P15) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (8)

Learning naive Bayes classifiers from positive and unlabelled examples with uncertainty ⋮ Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric ⋮ Estimating the class prior for positive and unlabelled data via logistic regression ⋮ A two-step anomaly detection based method for PU classification in imbalanced data sets ⋮ A survey on semi-supervised learning ⋮ Proper learning of \(k\)-term DNF formulas from satisfying assignments ⋮ Learning from positive and unlabeled data: a survey ⋮ On the relative value of weak information of supervision for learning generative models: an empirical study

Uses Software

• UCI-ml
• C4.5

Cites Work

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• Equivalence of models for polynomial learnability
• Text classification from labeled and unlabeled documents using EM
• A theory of the learnable
• Efficient noise-tolerant learning from statistical queries
• Noise-tolerant learning, the parity problem, and the statistical query model

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