Machine learningMachine learning
半监督逻辑回归
半监督逻辑回归通过在训练过程中整合未标记数据，扩展了标准逻辑分类器。它利用自训练、期望最大化或标签传播封装器，迭代地为未标记样本分配软标签并优化模型参数，从而在标记数据相对于整个数据集稀缺时提高泛化能力。
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来源
- Nigam, K., McCallum, A., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39, 103–134. DOI: 10.1023/a:1007692713085 ↗
- Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
如何引用本页
ScholarGate. (2026, June 3). Semi-supervised Logistic Regression (Self-training and EM-based variants). ScholarGate. https://scholargate.app/zh/machine-learning/semi-supervised-logistic-regression
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