Methoden vergleichen

	Active Learning Voting Ensemble	Aktives Lernen	Boosting	Semi-Supervised Learning
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning	Machine learning	Machine learning
Entstehungsjahr	1992	2009	1990–1997	1970s–2006 (formalized)
Urheber	Seung, H. S., Opper, M., & Sompolinsky, H.	Burr Settles	Schapire, R. E.; Freund, Y.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Typ	Active learning with ensemble voting	Interactive supervised learning framework	Sequential ensemble (iterative reweighting)	Learning paradigm
Wegweisende Quelle	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.	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Aliasnamen	Query by Committee, QBC, active ensemble learning, committee-based active learning	Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Verwandt	5	2	6	5
Zusammenfassung	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.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	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.