Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Pastiprināšana× | Stacking× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1990–1997 | 1992 |
| Autors≠ | Schapire, R. E.; Freund, Y. | Wolpert, D.H. |
| Tips≠ | Sequential ensemble (iterative reweighting) | Ensemble (heterogeneous meta-learning) |
| Pirmavots≠ | 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 ↗ | Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259. DOI ↗ |
| Citi nosaukumi≠ | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble | Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | 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. | Stacking, or stacked generalization, is an ensemble method introduced by David Wolpert in 1992 that combines the outputs of several different base models (Level-0) through a separate meta-model (Level-1). Unlike bagging and boosting, it deliberately uses heterogeneous model types, and it is the standard final-stage strategy in Kaggle competitions. |
| ScholarGateDatu kopa ↗ |
|
|