Linganisha mbinu

	Mashine ya Vektor Saidizi Imara (Robust Support Vector Machine)	Msitu Imara wa Misitu
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2006–2009	2000s–2010s
Mwanzilishi	Xu, H., Caramanis, C., & Mannor, S.	Various (extensions of Breiman 2001 Random Forest)
Aina	Robust supervised classifier / regressor	Robust Ensemble (noise-tolerant bagging of decision trees)
Chanzo asilia	Xu, H., Caramanis, C., & Mannor, S. (2009). Robustness and regularization of support vector machines. Journal of Machine Learning Research, 10, 1485–1510.	Chen, S., & Guestrin, C. (2019). Robust Random Forest. In Proceedings of the 36th International Conference on Machine Learning (ICML). Also see: Gao, W., & Zhou, Z.-H. (2013). On the Doubt about Margin Explanation of Boosting. Artificial Intelligence, 203, 1–18.
Majina mbadala	Robust SVM, RSVM, noise-tolerant SVM, outlier-robust SVM	RRF, noise-robust random forest, outlier-resistant random forest, robust ensemble forest
Zinazohusiana	5	6
Muhtasari	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.	Robust Random Forest extends the standard Random Forest ensemble by incorporating mechanisms that reduce the influence of outliers, label noise, and corrupted observations. Rather than treating all training instances equally, it applies weighting or filtering strategies so that noisy or anomalous samples contribute less to individual tree splits, yielding predictions that remain reliable even when data quality is imperfect.