Machine learningMachine learning
鲁棒单类支持向量机 (Robust One-Class SVM)
鲁棒单类支持向量机通过引入鲁棒性机制——例如截尾目标函数、鲁棒核函数选择或抗污染损失函数——来扩展经典的单类支持向量机（用于新颖性和异常检测），这些机制能够减弱训练数据中存在的重尾噪声或离群点的影响，从而产生一个能更好地代表正常类别真实支撑的决策边界。
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来源
- Scholkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., & Platt, J. (1999). Support vector method for novelty detection. Advances in Neural Information Processing Systems (NeurIPS), 12, 582–588. link ↗
- Liu, Y., Li, Z., & Zhou, C. (2018). Roseq: Robust and efficient one-class SVM for large-scale novelty detection. IEEE Transactions on Neural Networks and Learning Systems, 29(12), 6290–6304. link ↗
如何引用本页
ScholarGate. (2026, June 3). Robust One-Class Support Vector Machine. ScholarGate. https://scholargate.app/zh/machine-learning/robust-one-class-svm
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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