Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Pastiprināšana× | Balsošanas ansamblis× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1990–1997 | 1990s–2004 |
| Autors≠ | Schapire, R. E.; Freund, Y. | Lam & Suen; Kuncheva, L. I. (systematic treatment) |
| Tips≠ | Sequential ensemble (iterative reweighting) | Ensemble (combination of multiple classifiers by vote) |
| 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 ↗ | Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8 |
| Citi nosaukumi | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble | majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble |
| 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. | A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted. |
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