مقایسهٔ روشها
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| کیسهبندی آنلاین (Online Bagging)× | بگینگ (تجمیع بوتاسترپ)× | |
|---|---|---|
| حوزه | یادگیری ماشین | یادگیری ماشین |
| خانواده | Machine learning | Machine learning |
| سال پیدایش≠ | 2001 | 1996 |
| پدیدآور≠ | Oza, N. C. & Russell, S. | Breiman, L. |
| نوع≠ | Online ensemble (streaming bagging) | Ensemble meta-algorithm (variance reduction via bootstrap aggregation) |
| منبع بنیادین≠ | Oza, N. C., & Russell, S. (2001). Online bagging and boosting. In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001), pp. 105–112. link ↗ | Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140. DOI ↗ |
| نامهای دیگر≠ | incremental bagging, streaming bagging, online bootstrap aggregating, OzaBag | Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor |
| مرتبط≠ | 4 | 5 |
| خلاصه≠ | Online Bagging is a streaming ensemble method introduced by Oza and Russell in 2001 that adapts the classical bootstrap aggregating (Bagging) framework to the online learning setting. Instead of resampling a fixed dataset, each incoming instance is fed to every base learner a Poisson(1)-distributed number of times, faithfully approximating bootstrap sampling as the stream evolves. The result is a robust, incrementally updated ensemble that can handle concept drift and continuous data arrival without storing the entire dataset. | Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner. |
| ScholarGateمجموعهداده ↗ |
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