Machine learningMachine learning
正则化支持向量机
正则化支持向量机通过显式控制间隔最大化与训练误差之间的权衡来扩展经典支持向量机（SVM），其中通过 L1 或 L2 惩罚参数进行控制。Cortes 和 Vapnik 于 1995 年提出的软间隔（soft-margin）公式本身就是一个正则化模型，而后来的 L1-SVM 变体则进一步促进了特征稀疏性，从而在高维环境中实现了自动变量选择。
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来源
- Cortes, C. & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. DOI: 10.1007/BF00994018 ↗
- Zhu, J., Rosset, S., Tibshirani, R. & Hastie, T. (2004). 1-norm support vector machines. Advances in Neural Information Processing Systems (NIPS), 16. link ↗
如何引用本页
ScholarGate. (2026, June 3). Regularized Support Vector Machine (L1/L2-penalized SVM). ScholarGate. https://scholargate.app/zh/machine-learning/regularized-support-vector-machine
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