Võrdle meetodeid

	Ensemble Active Learning	Juhuslik mets	Poolitatud järelevalvega õppimine
Valdkond	Masinõpe	Masinõpe	Masinõpe
Perekond	Machine learning	Machine learning	Machine learning
Tekkeaasta	1992	2001	1970s–2006 (formalized)
Looja	Seung, H. S., Opper, M., & Sompolinsky, H.	Breiman, L.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Tüüp	Ensemble-based active learning strategy	Ensemble (bagging of decision trees)	Learning paradigm
Algallikas	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Rööpnimetused	Query by Committee, QBC active learning, committee-based active learning, ensemble query strategy	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Seotud	5	4	5
Kokkuvõte	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.	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.