方法证据记录
Robust Support Vector Machine
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.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Robust Support Vector Machine (Outlier-Resistant SVM)
分类方法记录 · ml-model / machine-learning
- Xu, H., Caramanis, C., & Mannor, S. (2009). Robustness and regularization of support vector machines. Journal of Machine Learning Research, 10, 1485–1510. · URL
- Collobert, R., Sinz, F., Weston, J., & Bottou, L. (2006). Trading convexity for scalability. Proceedings of the 23rd International Conference on Machine Learning (ICML), 201–208. · URL
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