On the relative value of weak information of supervision for learning generative models: an empirical study

DOI10.1016/j.ijar.2022.08.012OpenAlexW4293746542MaRDI QIDQ2092464

Jerónimo Hernández-González, Aritz Pérez

Publication date: 2 November 2022

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijar.2022.08.012

zbMATH Keywords

empirical studygenerative modelsmodel learningweak supervision

Mathematics Subject Classification ID

Reasoning under uncertainty in the context of artificial intelligence (68T37)

Cites Work

• Maximum likelihood estimation from fuzzy data using the EM algorithm
• Learning from partially supervised data using mixture models and belief functions
• Bayesian network classifiers
• A general framework for maximizing likelihood under incomplete data
• A survey on semi-supervised learning
• Parametric classification with soft labels using the evidential EM algorithm: linear discriminant analysis versus logistic regression
• Learning from positive and unlabeled examples
• The relative value of labeled and unlabeled samples in pattern recognition with an unknown mixing parameter
• Discrete Bayesian Network Classifiers
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