Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Онлайн LightGBM× | Онлайн-обучение× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2017 (LightGBM); 2000s (online boosting) | 1958–2000s |
| Автор метода≠ | Ke et al. (LightGBM); Bifet, Gavalda (online boosting theory) | Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors) |
| Тип≠ | Online ensemble (incremental gradient boosting) | Learning paradigm (sequential model update) |
| Основополагающий источник≠ | 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. link ↗ | Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI ↗ |
| Другие названия | Incremental LightGBM, LightGBM incremental training, streaming LightGBM, continual LightGBM | incremental learning, sequential learning, streaming learning, online machine learning |
| Связанные≠ | 5 | 6 |
| Сводка≠ | 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. | 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. |
| ScholarGateНабор данных ↗ |
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