পদ্ধতির তুলনা করুন

	Bayesian Active Learning	সক্রিয় শিখন (Active Learning)
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1992–2011	2009
প্রবর্তক	MacKay, D.J.C.; Houlsby, N. et al.	Burr Settles
ধরন	Active learning with Bayesian uncertainty	Interactive supervised learning framework
মৌলিক উৎস	Houlsby, N., Huszár, F., Ghahramani, Z., & Lengyel, M. (2011). Bayesian Active Learning for Classification and Preference Learning. arXiv preprint arXiv:1112.5745.	Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648.
অপর নাম	BAL, Bayesian optimal experimental design for ML, BALD (Bayesian Active Learning by Disagreement), probabilistic active learning	Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme
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.	Active learning is an iterative machine-learning paradigm in which a learning algorithm selectively queries an oracle — typically a human annotator — for labels on the most informative unlabeled examples. Formalized by Burr Settles in his seminal 2009 literature survey, active learning addresses the practical bottleneck of annotation cost by achieving high model accuracy with far fewer labeled examples than passive supervised learning requires.