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| 능동 학습 LightGBM× | LightGBM× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2017–present | 2017 |
| 창시자≠ | Settles, B. (active learning); Ke, G. et al. (LightGBM) | Ke, G. et al. (Microsoft) |
| 유형≠ | Hybrid (active learning query strategy + gradient boosting classifier) | Gradient boosting decision tree ensemble |
| 원전≠ | Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 6(1), 1–114. Morgan & Claypool. DOI ↗ | 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 (NeurIPS) 30, 3146–3154. link ↗ |
| 별칭 | AL-LightGBM, Active LightGBM, LightGBM active learning, AL-LGBM | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting |
| 관련 | 5 | 5 |
| 요약≠ | 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. | LightGBM is Microsoft's gradient boosting decision tree implementation, introduced by Ke and colleagues in 2017, that grows trees leaf-wise and bins features into histograms for speed. On large datasets it is much faster than XGBoost while retaining strong predictive accuracy. |
| ScholarGate데이터셋 ↗ |
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