Machine learningMachine learning

半监督梯度提升

半监督梯度提升将梯度提升树与自训练或伪标签相结合，以利用大量无标签数据和少量有标签数据。在有标签数据上初始拟合GBM后，将置信度高的预测分配给无标签样本；这些伪标签点被重新纳入训练，模型被重新提升，迭代直至收敛。这使得实践者能够在标签稀缺或昂贵时利用廉价的无标签数据。

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半监督梯度提升

Boosting 梯度提升（Gradient Boosting）自监督学习半监督学习半监督随机森林 XGBoost 在线梯度提升半监督 CatBoost 半监督 LightGBM

来源

Yarowsky, D. (1995). Unsupervised word sense disambiguation rivaling supervised methods. Proceedings of ACL 1995, 189–196. (Foundational self-training framework underlying pseudo-label approaches.) link ↗
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 Gradient Boosting (Self-training / Pseudo-labeling with Gradient Boosted Trees). ScholarGate. https://scholargate.app/zh/machine-learning/semi-supervised-gradient-boosting

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在线梯度提升半监督 CatBoost 半监督 LightGBM

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