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自监督梯度提升 (Self-supervised Gradient Boosting)

自监督梯度提升在经典梯度提升框架中融入了自监督的代理任务，以利用未标记数据。模型首先从无标注样本中学习有用的特征表示，然后利用这些表示来指导弱学习器的顺序集成，即使在标记样本稀缺的情况下也能实现强大的预测性能。

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自监督梯度提升 (Self-supervised Gradient Boosting)
梯度提升（Gradient Boosting）LightGBM随机森林半监督学习XGBoost自监督增强学习

来源

  1. Zhang, Y., Zhang, J., & Yang, Q. (2022). Self-Supervised Gradient Boosting for Semi-Supervised Learning on Tabular Data. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. link ↗
  2. Self-supervised learning. Wikipedia. link ↗

如何引用本页

ScholarGate. (2026, June 3). Self-supervised Gradient Boosting (SSL-GBM). ScholarGate. https://scholargate.app/zh/machine-learning/self-supervised-gradient-boosting

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自监督增强学习

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ScholarGate — Self-supervised Gradient Boosting (Self-supervised Gradient Boosting (SSL-GBM)). 于 2026-06-15 检索自 https://scholargate.app/zh/machine-learning/self-supervised-gradient-boosting · 数据集： https://doi.org/10.5281/zenodo.20539026