手法を比較

	オンラインLightGBM	LightGBM	オンライン学習
分野	機械学習	機械学習	機械学習
系統	Machine learning	Machine learning	Machine learning
提唱年	2017 (LightGBM); 2000s (online boosting)	2017	1958–2000s
提唱者	Ke et al. (LightGBM); Bifet, Gavalda (online boosting theory)	Ke, G. et al. (Microsoft)	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
種類	Online ensemble (incremental gradient boosting)	Gradient boosting decision tree ensemble	Learning paradigm (sequential model update)
原典	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.	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.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
別名	Incremental LightGBM, LightGBM incremental training, streaming LightGBM, continual LightGBM	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting	incremental learning, sequential learning, streaming learning, online machine learning
関連	5	5	6
概要	Online LightGBM applies the Light Gradient-Boosting Machine framework incrementally: instead of requiring all training data at once, the model is updated in mini-batches or data chunks as they arrive. This allows LightGBM's efficient histogram-based boosting to be deployed in streaming, continual-learning, and data-expansion scenarios without retraining from scratch.	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.	Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.