Salīdzināt metodes

	Aktīvā mācīšanās	Bagging (Bootstrap Aggregating)	Daudzpusīgā apguve
Nozare	Mašīnmācīšanās	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning	Machine learning
Izcelsmes gads	2009	1996	1970s–2006 (formalized)
Autors	Burr Settles	Breiman, L.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Tips	Interactive supervised learning framework	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Learning paradigm
Pirmavots	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.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Citi nosaukumi	Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Saistītās	2	5	5
Kopsavilkums	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.	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.