Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Aprendizagem Ativa× | LightGBM× | |
|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 2009 | 2017 |
| Autor original≠ | Burr Settles | Ke, G. et al. (Microsoft) |
| Tipo≠ | Interactive supervised learning framework | Gradient boosting decision tree ensemble |
| Fonte seminal≠ | Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648. link ↗ | Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q. & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems (NeurIPS) 30, 3146–3154. link ↗ |
| Outros nomes | Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting |
| Relacionados≠ | 2 | 5 |
| Resumo≠ | Active learning is an iterative machine-learning paradigm in which a learning algorithm selectively queries an oracle — typically a human annotator — for labels on the most informative unlabeled examples. Formalized by Burr Settles in his seminal 2009 literature survey, active learning addresses the practical bottleneck of annotation cost by achieving high model accuracy with far fewer labeled examples than passive supervised learning requires. | LightGBM is Microsoft's gradient boosting decision tree implementation, introduced by Ke and colleagues in 2017, that grows trees leaf-wise and bins features into histograms for speed. On large datasets it is much faster than XGBoost while retaining strong predictive accuracy. |
| ScholarGateConjunto de dados ↗ |
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