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| 勾配ブースティング× | 半教師ありブースティング× | |
|---|---|---|
| 分野 | 機械学習 | 機械学習 |
| 系統 | Machine learning | Machine learning |
| 提唱年≠ | 2001 | 1999–2009 |
| 提唱者≠ | Friedman, J. H. | Mallapragada, P. K.; Bennett, K. P.; and others |
| 種類≠ | Ensemble (sequential boosting of decision trees) | Semi-supervised ensemble method |
| 原典≠ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | 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 ↗ |
| 別名 | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting |
| 関連 | 5 | 5 |
| 概要≠ | Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost. | 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. |
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