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| Bayesiansk modellmedling (BMA)× | Boosting× | |
|---|---|---|
| Ämnesområde≠ | Bayesiansk statistik | Maskininlärning |
| Familj≠ | Bayesian methods | Machine learning |
| Ursprungsår≠ | 1999 | 1990–1997 |
| Upphovsperson≠ | Hoeting, Madigan, Raftery & Volinsky | Schapire, R. E.; Freund, Y. |
| Typ≠ | Bayesian model averaging | Sequential ensemble (iterative reweighting) |
| Ursprungskälla≠ | Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382–401. 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 ↗ |
| Alias≠ | BMA, Bayesian model combination, Bayesian Model Ortalaması (BMA) | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble |
| Närliggande≠ | 5 | 6 |
| Sammanfattning≠ | Bayesian Model Averaging (BMA), formalised as a tutorial by Hoeting, Madigan, Raftery and Volinsky in 1999, addresses model uncertainty by averaging over all plausible model specifications rather than selecting a single best model. Each candidate model receives a posterior probability that reflects how well it fits the data given a prior, and predictions or coefficient estimates are formed as weighted averages across the entire model space. This approach reduces the bias and overconfidence that arise when a single selected model is treated as the true one. | 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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