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自监督随机森林

自监督随机森林（SSL-RF）将经典的随机森林扩展到标记样本稀缺的场景。森林首先使用从自监督前置任务派生的自动生成伪标签进行训练——例如，预测数据转换或掩码特征——然后根据可用的真实标签进行精炼，将自监督学习的标签效率与集成树的鲁棒性相结合。

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自监督随机森林
决策树标签传播随机森林自监督学习半监督学习XGBoost

来源

  1. Lefortier, D., Chitta, K., & Agrawal, P. (2022). Self-supervised random forests. arXiv:2204.01430. link ↗
  2. Criminisi, A., Shotton, J., & Konukoglu, E. (2012). Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Foundations and Trends in Computer Graphics and Vision, 7(2–3), 81–227. DOI: 10.1561/0600000035 ↗

如何引用本页

ScholarGate. (2026, June 3). Self-supervised Random Forest (SSL-RF). ScholarGate. https://scholargate.app/zh/machine-learning/self-supervised-random-forest

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ScholarGate — Self-supervised Random Forest (Self-supervised Random Forest (SSL-RF)). 于 2026-06-15 检索自 https://scholargate.app/zh/machine-learning/self-supervised-random-forest · 数据集： https://doi.org/10.5281/zenodo.20539026