Machine learningMachine learning
半监督 LightGBM
半监督 LightGBM 将 LightGBM 高效的梯度提升框架与半监督策略（最常见的是伪标签或自训练）相结合，以利用大量无标签数据和少量有标签数据，在获取标签成本高昂或耗时时提高预测性能。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
来源
- Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154. 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 Learning with Light Gradient Boosting Machine. ScholarGate. https://scholargate.app/zh/machine-learning/semi-supervised-lightgbm
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
Compare side by side →