方法对比
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| 在线提升 (Online Boosting)× | 半监督提升× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2001 | 1999–2009 |
| 提出者≠ | Oza, N. C. & Russell, S. | Mallapragada, P. K.; Bennett, K. P.; and others |
| 类型≠ | Online ensemble (incremental boosting) | Semi-supervised ensemble method |
| 开创性文献≠ | Oza, N. C., & Russell, S. (2001). Online Bagging and Boosting. In Artificial Intelligence and Statistics 2001 (pp. 105–112). Morgan Kaufmann. link ↗ | Mallapragada, P. K., Jin, R., Jain, A. K., & Liu, Y. (2009). SemiBoost: Boosting for Semi-supervised Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(11), 2000–2014. DOI ↗ |
| 别名 | streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting | SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting |
| 相关≠ | 6 | 5 |
| 摘要≠ | Online Boosting adapts the classical boosting framework to data streams, updating an ensemble of weak learners one example at a time without storing the full dataset. The Oza-Russell formulation approximates AdaBoost's reweighting using Poisson-sampled instance counts, enabling accurate, adaptive classification in real-time or resource-constrained environments. | Semi-supervised Boosting is an ensemble learning paradigm that extends classical boosting algorithms — such as AdaBoost — to exploit both labeled and unlabeled data. By propagating label information through a similarity structure over unlabeled instances, it trains stronger classifiers than supervised boosting alone when labeled data are scarce. |
| ScholarGate数据集 ↗ |
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