Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Robustais atbalsta vektoru mašīnas (Robust SVM)× | Regularizēta atbalsta vektoru mašīna× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2006–2009 | 1995–2004 |
| Autors≠ | Xu, H., Caramanis, C., & Mannor, S. | Cortes, C. & Vapnik, V. (soft-margin SVM); Zhu et al. (L1-SVM) |
| Tips≠ | Robust supervised classifier / regressor | Regularized discriminative classifier / regressor |
| Pirmavots≠ | Xu, H., Caramanis, C., & Mannor, S. (2009). Robustness and regularization of support vector machines. Journal of Machine Learning Research, 10, 1485–1510. link ↗ | Cortes, C. & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. DOI ↗ |
| Citi nosaukumi | Robust SVM, RSVM, noise-tolerant SVM, outlier-robust SVM | Regularized SVM, L1-SVM, L2-SVM, penalized SVM |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | 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. | Regularized Support Vector Machine extends the classic SVM by explicitly controlling the trade-off between margin maximization and training error through an L1 or L2 penalty parameter. The soft-margin formulation introduced by Cortes and Vapnik in 1995 is itself a regularized model, and later L1-SVM variants additionally promote feature sparsity, enabling automatic variable selection in high-dimensional settings. |
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