Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Zesilování× | FP-Růst (Růst častých vzorů)× | |
|---|---|---|
| Obor | Strojové učení | Strojové učení |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 1990–1997 | 2000 |
| Tvůrce≠ | Schapire, R. E.; Freund, Y. | Jiawei Han, Jian Pei & Yiwen Yin |
| Typ≠ | Sequential ensemble (iterative reweighting) | Frequent-itemset mining algorithm |
| Původní zdroj≠ | 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 ↗ | Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12. DOI ↗ |
| Další názvy | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble | frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme |
| Příbuzné≠ | 6 | 4 |
| Shrnutí≠ | 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. | FP-Growth, introduced by Jiawei Han, Jian Pei, and Yiwen Yin in 2000, mines frequent itemsets from transaction data without generating candidate sets, the costly step that slows the classic Apriori algorithm. It compresses the database into a frequent-pattern tree (FP-tree) in two scans, then grows frequent patterns recursively from that structure, making it dramatically faster than Apriori on large, dense datasets. |
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