Vertaile menetelmiä
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| Bayesiläinen säkittämine× | Boosting× | |
|---|---|---|
| Tieteenala | Koneoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2001 | 1990–1997 |
| Kehittäjä≠ | Clyde, M. & Lee, H. (building on Rubin's Bayesian bootstrap, 1981) | Schapire, R. E.; Freund, Y. |
| Tyyppi≠ | Ensemble (Bayesian bootstrap aggregation) | Sequential ensemble (iterative reweighting) |
| Alkuperäislähde≠ | Clyde, M. & Lee, H. (2001). Bagging and the Bayesian bootstrap. In T. Richardson & T. Jaakkola (Eds.), Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001). link ↗ | Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. DOI ↗ |
| Rinnakkaisnimet | Bayesian bootstrap aggregation, BB-ensemble, Bayesian model averaging via bootstrap, Bayesian bagged ensemble | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble |
| Liittyvät | 6 | 6 |
| Tiivistelmä≠ | Bayesian Bagging replaces the classical bootstrap with the Bayesian bootstrap — drawing Dirichlet-distributed weights over training observations rather than sampling with replacement — and trains an ensemble of base learners under those weights. The result is a principled ensemble that approximates a Bayesian posterior over predictions, yielding calibrated uncertainty estimates alongside strong predictive accuracy. | Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy. |
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