Porovnať metódy

	Aktívne učenie	Náhodný les	Polosupervizované učenie
Odbor	Strojové učenie	Strojové učenie	Strojové učenie
Rodina	Machine learning	Machine learning	Machine learning
Rok vzniku	2009	2001	1970s–2006 (formalized)
Tvorca	Burr Settles	Breiman, L.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Typ	Interactive supervised learning framework	Ensemble (bagging of decision trees)	Learning paradigm
Pôvodný zdroj	Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648.	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
Ďalšie názvy	Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Príbuzné	2	4	5
Zhrnutie	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.	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.