Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Бустинг× | FP-Рост (Рост часто встречаемых паттернов)× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 1990–1997 | 2000 |
| Автор метода≠ | Schapire, R. E.; Freund, Y. | Jiawei Han, Jian Pei & Yiwen Yin |
| Тип≠ | Sequential ensemble (iterative reweighting) | Frequent-itemset mining algorithm |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble | frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme |
| Связанные≠ | 6 | 4 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
|
|