Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Robust Support Vector Machine× | One-class SVM× | |
|---|---|---|
| Ämnesområde | Maskininlärning | Maskininlärning |
| Familj | Machine learning | Machine learning |
| Ursprungsår≠ | 2006–2009 | 1999–2001 |
| Upphovsperson≠ | Xu, H., Caramanis, C., & Mannor, S. | Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C. |
| Typ≠ | Robust supervised classifier / regressor | Anomaly / novelty detection (unsupervised) |
| Ursprungskälla≠ | Xu, H., Caramanis, C., & Mannor, S. (2009). Robustness and regularization of support vector machines. Journal of Machine Learning Research, 10, 1485–1510. link ↗ | Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471. DOI ↗ |
| Alias | Robust SVM, RSVM, noise-tolerant SVM, outlier-robust SVM | OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM |
| Närliggande≠ | 5 | 3 |
| Sammanfattning≠ | 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. | One-class SVM is an unsupervised anomaly and novelty detection algorithm that learns a tight boundary around normal training data in a kernel-induced feature space, flagging new observations that fall outside that boundary as outliers. Introduced by Scholkopf et al. in 1999–2001, it extends the SVM framework to the single-class setting where no labelled anomalies are available. |
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