เปรียบเทียบวิธี

	Bayesian Active Learning	Bayesian Optimization
สาขาวิชา	การเรียนรู้ของเครื่อง	การหาค่าเหมาะที่สุด
ตระกูล	Machine learning	Process / pipeline
ปีกำเนิด	1992–2011	1975 (foundational); 2012 (ML standard)
ผู้ริเริ่ม	MacKay, D.J.C.; Houlsby, N. et al.	Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012)
ประเภท	Active learning with Bayesian uncertainty	Sequential model-based black-box optimization
แหล่งต้นตำรับ	Houlsby, N., Huszár, F., Ghahramani, Z., & Lengyel, M. (2011). Bayesian Active Learning for Classification and Preference Learning. arXiv preprint arXiv:1112.5745.	Snoek, J., Larochelle, H., & Adams, R.P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems (NeurIPS), 25.
ชื่อเรียกอื่น	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
ที่เกี่ยวข้อง	6	2
สรุป	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.