Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Boosting× | FP-Growth (Frequent Pattern Growth)× | |
|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 1990–1997 | 2000 |
| Autor original≠ | Schapire, R. E.; Freund, Y. | Jiawei Han, Jian Pei & Yiwen Yin |
| Tipo≠ | Sequential ensemble (iterative reweighting) | Frequent-itemset mining algorithm |
| Fonte seminal≠ | 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 ↗ |
| Outros nomes | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble | frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme |
| Relacionados≠ | 6 | 4 |
| Resumo≠ | 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. |
| ScholarGateConjunto de dados ↗ |
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