পদ্ধতির তুলনা করুন

	এনসেম্বল অ্যাক্টিভ লার্নিং	আধা-তত্ত্বাবধানাধীন শিখন (Semi-supervised Learning)
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1992	1970s–2006 (formalized)
প্রবর্তক	Seung, H. S., Opper, M., & Sompolinsky, H.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
ধরন	Ensemble-based active learning strategy	Learning paradigm
মৌলিক উৎস	Seung, H. S., Opper, M., & Sompolinsky, H. (1992). Query by committee. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory (COLT 1992), pp. 287–294. ACM.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
অপর নাম	Query by Committee, QBC active learning, committee-based active learning, ensemble query strategy	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
সম্পর্কিত	5	5
সারসংক্ষেপ	Ensemble Active Learning combines a committee of diverse models with an active learning loop to select the most informative unlabeled examples for labeling. Rooted in the Query by Committee framework introduced by Seung et al. (1992), it uses disagreement among committee members as a signal for uncertainty, reducing the number of labeled examples needed to achieve strong predictive performance.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.