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| Bayesian Semi-supervised Learning× | Bayesian Active Learning× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2003–2006 | 1992–2011 |
| Urheber≠ | Chapelle, Scholkopf & Zien; Zhu, Ghahramani & Lafferty | MacKay, D.J.C.; Houlsby, N. et al. |
| Typ≠ | Probabilistic semi-supervised framework | Active learning with Bayesian uncertainty |
| Wegweisende Quelle≠ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 | Houlsby, N., Huszár, F., Ghahramani, Z., & Lengyel, M. (2011). Bayesian Active Learning for Classification and Preference Learning. arXiv preprint arXiv:1112.5745. link ↗ |
| Aliasnamen | Bayesian SSL, probabilistic semi-supervised learning, generative semi-supervised model, Bayesian transductive learning | BAL, Bayesian optimal experimental design for ML, BALD (Bayesian Active Learning by Disagreement), probabilistic active learning |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | Bayesian semi-supervised learning is a probabilistic framework that uses both a small labeled dataset and a larger pool of unlabeled observations to infer model parameters and make predictions. By treating missing labels as latent variables and placing priors over parameters, it naturally quantifies uncertainty while leveraging unlabeled data to improve generalization. | Bayesian Active Learning (BAL) combines a probabilistic model with an active query strategy to identify the unlabeled examples that, once labeled, would most reduce model uncertainty. Instead of labeling data at random, BAL guides an oracle — typically a human annotator — toward the points where labeling will provide the greatest information gain, making it highly label-efficient. |
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