Compară metode

	Învățare online robustă	Învățare activă
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	2000s–2010s	2009
Autorul original	Hazan, E.; Shalev-Shwartz, S.; and others	Burr Settles
Tip	Algorithmic framework	Interactive supervised learning framework
Sursa seminală	Hazan, E. (2016). Introduction to Online Convex Optimization. Foundations and Trends in Optimization, 2(3–4), 157–325.	Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648.
Denumiri alternative	ROL, robust incremental learning, adversarially robust online learning, robust sequential learning	Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme
Înrudite	5	2
Rezumat	Robust Online Learning extends the online learning framework — where a model updates sequentially after each observation — by incorporating robustness mechanisms that guard against corrupted labels, adversarial examples, heavy-tailed noise, and concept drift. The result is a sequential learner that maintains bounded regret even when the data stream contains outliers or deliberate perturbations.	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.