Machine learning
支持向量回归
支持向量回归（Support Vector Regression, SVR）由Smola和Schölkopf在2004年的教程中阐述，它通过拟合一个函数来预测连续结果，该函数在数据周围的ε宽度管内保持，同时尽可能减少误差。它将支持向量机的思想从分类扩展到回归，使用核函数来捕捉非线性关系。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Smola, A.J. & Schölkopf, B. (2004). A Tutorial on Support Vector Regression. Statistics and Computing, 14, 199–222. DOI: 10.1023/B:STCO.0000035301.49549.88 ↗
如何引用本页
ScholarGate. (2026, June 1). Support Vector Regression (SVR). ScholarGate. https://scholargate.app/zh/machine-learning/svm-regression
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.
- K-Nearest Neighbors机器学习↔ compare
- Lasso 回归机器学习↔ compare
- 岭回归（Ridge Regression）机器学习↔ compare
- 支持向量机（分类）机器学习↔ compare