Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Robustais atbalsta vektoru mašīnas (Robust SVM)× | Robustais gradientu pastiprinājums× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2006–2009 | 2001 |
| Autors≠ | Xu, H., Caramanis, C., & Mannor, S. | Friedman, J. H. (with Huber loss from Huber, P. J.) |
| Tips≠ | Robust supervised classifier / regressor | Ensemble (boosted trees with robust loss) |
| Pirmavots≠ | Xu, H., Caramanis, C., & Mannor, S. (2009). Robustness and regularization of support vector machines. Journal of Machine Learning Research, 10, 1485–1510. link ↗ | Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Citi nosaukumi | Robust SVM, RSVM, noise-tolerant SVM, outlier-robust SVM | gradient boosting with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees |
| Saistītās≠ | 5 | 6 |
| 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. | Robust Gradient Boosting is gradient boosting trained with outlier-resistant loss functions — most commonly the Huber loss or quantile (pinball) loss — instead of squared-error loss. Proposed in Friedman's seminal 2001 paper, this variant produces predictions far less distorted by extreme values or contaminated labels, while retaining the full predictive power of gradient-boosted trees. |
| ScholarGateDatu kopa ↗ |
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