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| Bayesowskie aktywne uczenie się× | Proces Gaussa× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1992–2011 | 2006 (book); roots in Kriging, 1951) |
| Twórca≠ | MacKay, D.J.C.; Houlsby, N. et al. | Rasmussen, C. E. & Williams, C. K. I. |
| Typ≠ | Active learning with Bayesian uncertainty | Probabilistic non-parametric model |
| Źródło pierwotne≠ | Houlsby, N., Huszár, F., Ghahramani, Z., & Lengyel, M. (2011). Bayesian Active Learning for Classification and Preference Learning. arXiv preprint arXiv:1112.5745. link ↗ | Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9 |
| Inne nazwy | BAL, Bayesian optimal experimental design for ML, BALD (Bayesian Active Learning by Disagreement), probabilistic active learning | GP, Gaussian Process Regression, GPR, Kriging |
| Pokrewne≠ | 6 | 3 |
| Podsumowanie≠ | 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. | A Gaussian Process (GP) is a non-parametric, fully probabilistic machine learning model that places a prior distribution directly over functions. Rather than predicting a single value, it returns a predictive mean and a calibrated uncertainty estimate at every test point, making it especially valuable for regression on small to medium datasets and for Bayesian optimization tasks. |
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