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| 베이즈 능동 학습× | 베이지안 최적화× | |
|---|---|---|
| 분야≠ | 머신러닝 | 최적화 |
| 계열≠ | Machine learning | Process / pipeline |
| 기원 연도≠ | 1992–2011 | 1975 (foundational); 2012 (ML standard) |
| 창시자≠ | MacKay, D.J.C.; Houlsby, N. et al. | Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012) |
| 유형≠ | Active learning with Bayesian uncertainty | Sequential model-based black-box optimization |
| 원전≠ | Houlsby, N., Huszár, F., Ghahramani, Z., & Lengyel, M. (2011). Bayesian Active Learning for Classification and Preference Learning. arXiv preprint arXiv:1112.5745. link ↗ | Snoek, J., Larochelle, H., & Adams, R.P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems (NeurIPS), 25. link ↗ |
| 별칭 | BAL, Bayesian optimal experimental design for ML, BALD (Bayesian Active Learning by Disagreement), probabilistic active learning | Bayesçi Optimizasyon (Hyperparameter Tuning), surrogate-based optimization, sequential model-based optimization, SMBO |
| 관련≠ | 6 | 2 |
| 요약≠ | 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. | Bayesian Optimization is a sequential, model-based strategy for finding the optimum of expensive black-box functions with as few evaluations as possible. Rooted in the work of Mockus (1975) and brought to mainstream machine-learning practice by Snoek, Larochelle, and Adams (2012), it fits a probabilistic surrogate model — typically a Gaussian Process — to past observations and uses an acquisition function to decide where to probe next, balancing exploration of unknown regions with exploitation of promising ones. |
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