Methoden vergleichen

	Active Learning Boosting	Semi-Supervised Learning
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	1998	1970s–2006 (formalized)
Urheber	Abe, N. & Mamitsuka, H.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Typ	Hybrid active-learning ensemble	Learning paradigm
Wegweisende Quelle	Abe, N. & Mamitsuka, H. (1998). Query Learning Strategies Using Boosting and Bagging. Proceedings of the 15th International Conference on Machine Learning (ICML 1998), pp. 1–9. Morgan Kaufmann.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Aliasnamen	boosting-based active learning, query learning with boosting, active boosting, ensemble active learning	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Verwandt	4	5
Zusammenfassung	Active Learning Boosting combines the query-driven label acquisition of active learning with the weighted-ensemble logic of boosting algorithms such as AdaBoost. The model iteratively selects the most informative unlabeled examples to annotate — guided by the disagreement or uncertainty within the boosting ensemble — and retrains after each new label, achieving high accuracy with far fewer labeled examples than passive learning.	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.