Salīdzināt metodes

	Robust Online Learning	Daudzpusīgā tiešsaistes apguve
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2000s–2010s	2000s–2010s
Autors	Hazan, E.; Shalev-Shwartz, S.; and others	Goldberg, A.; Li, M.; Zhu, X. (among key contributors)
Tips	Algorithmic framework	Hybrid learning paradigm (online + semi-supervised)
Pirmavots	Hazan, E. (2016). Introduction to Online Convex Optimization. Foundations and Trends in Optimization, 2(3–4), 157–325.	Goldberg, A., Li, M., & Zhu, X. (2008). Online manifold regularization: A new learning setting and empirical study. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2008), Lecture Notes in Computer Science, 5211, 393–407. Springer.
Citi nosaukumi	ROL, robust incremental learning, adversarially robust online learning, robust sequential learning	SSOL, online semi-supervised learning, semi-supervised incremental learning, streaming semi-supervised learning
Saistītās	5	4
Kopsavilkums	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.	Semi-supervised Online Learning combines the incremental update style of online learning with the ability to exploit unlabeled examples, enabling models to improve continuously from a data stream in which only a small fraction of arriving instances carry ground-truth labels. It is especially valuable when labeling is expensive or delayed but data arrives in real time.