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| 로버스트 가우시안 프로세스× | 로버스트 서포트 벡터 머신× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2011 (formal treatment); GP foundations: Rasmussen & Williams 2006 | 2006–2009 |
| 창시자≠ | Jylanki, P.; Vanhatalo, J.; Vehtari, A. | Xu, H., Caramanis, C., & Mannor, S. |
| 유형≠ | Probabilistic non-parametric regression / classification | Robust supervised classifier / regressor |
| 원전≠ | Jylanki, P., Vanhatalo, J., & Vehtari, A. (2011). Robust Gaussian Process Regression with a Student-t Likelihood. Journal of Machine Learning Research, 12, 3227–3257. link ↗ | Xu, H., Caramanis, C., & Mannor, S. (2009). Robustness and regularization of support vector machines. Journal of Machine Learning Research, 10, 1485–1510. link ↗ |
| 별칭 | Robust GP, Student-t Process, Heavy-tailed Gaussian Process, Outlier-robust GP | Robust SVM, RSVM, noise-tolerant SVM, outlier-robust SVM |
| 관련 | 5 | 5 |
| 요약≠ | Robust Gaussian Process (Robust GP) extends the standard Gaussian Process framework by replacing the Gaussian noise likelihood with a heavy-tailed distribution — typically Student-t — so that outliers in the training data exert less influence on the learned function. It retains the full probabilistic, uncertainty-quantifying character of a standard GP while becoming far less sensitive to corrupted or anomalous observations. | 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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