Machine learningMachine learning
主动学习 LightGBM
主动学习 LightGBM 将主动学习的查询效率标签选择策略与 LightGBM 的速度和准确性相结合，LightGBM 是一个基于直方图的梯度提升框架。该模型会迭代地选择信息量最大的未标记实例进行人工标注，在不断增长的标记数据集上重新训练 LightGBM，并以远少于被动监督学习的标记样本达到高准确率。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 6(1), 1–114. Morgan & Claypool. DOI: 10.2200/S00429ED1V01Y201207AIM018 ↗
- 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 ↗
如何引用本页
ScholarGate. (2026, June 3). Active Learning with Light Gradient Boosting Machine. ScholarGate. https://scholargate.app/zh/machine-learning/active-learning-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.
- 主动学习机器学习↔ compare
- 梯度提升（Gradient Boosting）机器学习↔ compare
- LightGBM机器学习↔ compare
- 随机森林机器学习↔ compare
- XGBoost机器学习↔ compare