Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| LSTM× | XGBoost× | |
|---|---|---|
| Área≠ | Aprendizado profundo | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 1997 | 2016 |
| Autor original≠ | Hochreiter, S. & Schmidhuber, J. | Chen, T. & Guestrin, C. |
| Tipo≠ | Recurrent neural network (gated memory cell) | Ensemble (gradient-boosted decision trees) |
| Fonte seminal≠ | Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. DOI ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Outros nomes≠ | LSTM (Uzun Kısa Dönem Bellek Ağı), long short-term memory, LSTM network, recurrent neural network with memory cells | XGBoost, extreme gradient boosting, scalable tree boosting |
| Relacionados | 5 | 5 |
| Resumo≠ | LSTM (Long Short-Term Memory) is a recurrent neural network architecture, introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997, that can learn long-term dependencies in sequential data and is widely used for time-series and sequence prediction. It keeps an internal memory that lets information persist across many time steps. | XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions. |
| ScholarGateConjunto de dados ↗ |
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