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| Robust One-Class SVM× | Robust Support Vector-maskine× | |
|---|---|---|
| Fagområde | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 2000s–2010s | 2006–2009 |
| Ophavsperson≠ | Extensions of Scholkopf et al. (1999); robust variants developed in 2000s–2010s | Xu, H., Caramanis, C., & Mannor, S. |
| Type≠ | Anomaly detection / novelty detection | Robust supervised classifier / regressor |
| Oprindelig kilde≠ | Scholkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., & Platt, J. (1999). Support vector method for novelty detection. Advances in Neural Information Processing Systems (NeurIPS), 12, 582–588. 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 | Robust OCSVM, Outlier-robust One-Class SVM, Contamination-tolerant OCSVM, Robust novelty detection SVM | Robust SVM, RSVM, noise-tolerant SVM, outlier-robust SVM |
| Relaterede | 5 | 5 |
| Resumé≠ | Robust One-Class SVM extends the classic One-Class Support Vector Machine for novelty and anomaly detection by incorporating robustness mechanisms — such as trimmed objectives, robust kernel choices, or contamination-tolerant loss functions — that reduce the influence of heavy-tailed noise or outliers present in the training data, yielding a decision boundary that better represents the true support of the normal class. | 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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