Machine learningMachine learning
半监督线性回归
半监督线性回归在小型标记数据集上拟合线性模型，然后利用大量未标记观测数据来改进系数估计和泛化能力。通过为未标记点生成伪标签并迭代优化模型，它比仅使用稀缺标记数据训练的纯监督线性模型能实现更好的预测准确性。
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来源
- Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
- Zhou, Z.-H., & Li, M. (2005). Semi-supervised regression with co-training. Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI), 908–913. link ↗
如何引用本页
ScholarGate. (2026, June 3). Semi-supervised Linear Regression (Linear Model with Labeled and Unlabeled Data). ScholarGate. https://scholargate.app/zh/machine-learning/semi-supervised-linear-regression
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