Salīdzināt metodes

	Robust Online Learning	Robustais atbalsta vektoru mašīnas (Robust SVM)
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2000s–2010s	2006–2009
Autors	Hazan, E.; Shalev-Shwartz, S.; and others	Xu, H., Caramanis, C., & Mannor, S.
Tips	Algorithmic framework	Robust supervised classifier / regressor
Pirmavots	Hazan, E. (2016). Introduction to Online Convex Optimization. Foundations and Trends in Optimization, 2(3–4), 157–325.	Xu, H., Caramanis, C., & Mannor, S. (2009). Robustness and regularization of support vector machines. Journal of Machine Learning Research, 10, 1485–1510.
Citi nosaukumi	ROL, robust incremental learning, adversarially robust online learning, robust sequential learning	Robust SVM, RSVM, noise-tolerant SVM, outlier-robust SVM
Saistītās	5	5
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.	Robust SVM extends the standard support vector machine to resist the influence of outliers and mislabeled points. By replacing the hinge loss with a bounded or non-convex loss function — or by incorporating robust optimization constraints — it learns a decision boundary that is far less distorted by corrupted training examples, making it suitable for noisy real-world datasets where standard SVM would degrade significantly.