विधियों की तुलना करें

	बायेसियन सक्रिय शिक्षण	बेयसियन ऑप्टिमाइजेशन
क्षेत्र	मशीन अधिगम	अनुकूलन
परिवार	Machine learning	Process / pipeline
उद्भव वर्ष	1992–2011	1975 (foundational); 2012 (ML standard)
प्रवर्तक	MacKay, D.J.C.; Houlsby, N. et al.	Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012)
प्रकार	Active learning with Bayesian uncertainty	Sequential model-based black-box optimization
मौलिक स्रोत	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.
उपनाम	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
संबंधित	6	2
सारांश	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.