পদ্ধতির তুলনা করুন

	সক্রিয় শিখন ভোটিং এনসেম্বল	সক্রিয় শিখন (Active Learning)	Bagging (Bootstrap Aggregating)
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning	Machine learning
উদ্ভবের বছর	1992	2009	1996
প্রবর্তক	Seung, H. S., Opper, M., & Sompolinsky, H.	Burr Settles	Breiman, L.
ধরন	Active learning with ensemble voting	Interactive supervised learning framework	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)
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 '92), pp. 287–294. ACM.	Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.
অপর নাম	Query by Committee, QBC, active ensemble learning, committee-based active learning	Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor
সম্পর্কিত	5	2	5
সারসংক্ষেপ	Active Learning Voting Ensemble — formally known as Query by Committee — is an active learning strategy that trains a committee of diverse models and selects the unlabeled examples where the committee members disagree most for human annotation. By focusing labeling effort on the most informative points, it achieves high accuracy with far fewer labeled examples than passive learning requires.	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.	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.