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| Robust Online Læring× | Robust Support Vector-maskine× | |
|---|---|---|
| Fagområde | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 2000s–2010s | 2006–2009 |
| Ophavsperson≠ | Hazan, E.; Shalev-Shwartz, S.; and others | Xu, H., Caramanis, C., & Mannor, S. |
| Type≠ | Algorithmic framework | Robust supervised classifier / regressor |
| Oprindelig kilde≠ | Hazan, E. (2016). Introduction to Online Convex Optimization. Foundations and Trends in Optimization, 2(3–4), 157–325. link ↗ | Xu, H., Caramanis, C., & Mannor, S. (2009). Robustness and regularization of support vector machines. Journal of Machine Learning Research, 10, 1485–1510. link ↗ |
| Aliasser | ROL, robust incremental learning, adversarially robust online learning, robust sequential learning | Robust SVM, RSVM, noise-tolerant SVM, outlier-robust SVM |
| Relaterede | 5 | 5 |
| Resumé≠ | 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. |
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