Salīdzināt metodes

	Robust Active Learning	Daudzpusīgā apguve
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2006	1970s–2006 (formalized)
Autors	Balcan, M.-F.; Beygelzimer, A.; Langford, J.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Tips	Active learning with robustness guarantees	Learning paradigm
Pirmavots	Balcan, M.-F., Beygelzimer, A., & Langford, J. (2006). Agnostic active learning. In Proceedings of the 23rd International Conference on Machine Learning (ICML 2006), pp. 65–72. ACM.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Citi nosaukumi	RAL, noise-tolerant active learning, robust query learning, adversarially robust active learning	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Saistītās	6	5
Kopsavilkums	Robust Active Learning extends the standard active learning framework to handle noisy labels, adversarial perturbations, and unreliable oracles. Rather than assuming perfect labeling, it incorporates statistical or adversarial robustness guarantees into the query selection process, maintaining sample efficiency while tolerating corruption in the annotation process.	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.