পদ্ধতির তুলনা করুন

	Bayesian Active Learning	বেয়েশিয়ান অপ্টিমাইজেশান
ক্ষেত্র	যন্ত্র শিখন	অনুকূলকরণ
পরিবার	Machine learning	Process / pipeline
উদ্ভবের বছর	1992–2011	1975 (foundational); 2012 (ML standard)
প্রবর্তক	MacKay, D.J.C.; Houlsby, N. et al.	Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012)
ধরন	Active learning with Bayesian uncertainty	Sequential model-based black-box optimization
মৌলিক উৎস	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.
অপর নাম	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
সম্পর্কিত	6	2
সারসংক্ষেপ	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.