方法证据记录
Active Learning LightGBM
Active Learning LightGBM couples the query-efficient label-selection strategy of active learning with the speed and accuracy of LightGBM, a histogram-based gradient boosting framework. The model iteratively selects the most informative unlabeled instances for human annotation, retrains LightGBM on the growing labeled set, and converges to high accuracy with far fewer labeled examples than passive supervised learning.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Active Learning with Light Gradient Boosting Machine
分类方法记录 · ml-model / machine-learning
- 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. · URL
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