Machine learningMachine learning
鲁棒支持向量机
鲁棒支持向量机（Robust SVM）是对标准支持向量机（SVM）的扩展，旨在抵抗异常值和错误标记点的影响。通过将合页损失（hinge loss）替换为有界或非凸损失函数，或通过引入鲁棒优化约束，它能够学习到一个受损坏训练样本扭曲程度大大减小的决策边界，因此适用于标准SVM会显著退化的、存在噪声的真实世界数据集。
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来源
- Xu, H., Caramanis, C., & Mannor, S. (2009). Robustness and regularization of support vector machines. Journal of Machine Learning Research, 10, 1485–1510. link ↗
- 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. link ↗
如何引用本页
ScholarGate. (2026, June 3). Robust Support Vector Machine (Outlier-Resistant SVM). ScholarGate. https://scholargate.app/zh/machine-learning/robust-support-vector-machine
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