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| Perceptron wielowarstwowy (MLP)× | XGBoost× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1986 | 2016 |
| Twórca≠ | Rumelhart, D. E., Hinton, G. E., & Williams, R. J. | Chen, T. & Guestrin, C. |
| Typ≠ | Feedforward neural network (supervised learning) | Ensemble (gradient-boosted decision trees) |
| Źródło pierwotne≠ | Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536. DOI ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Inne nazwy≠ | MLP, feedforward neural network, fully connected neural network, artificial neural network | XGBoost, extreme gradient boosting, scalable tree boosting |
| Pokrewne≠ | 4 | 5 |
| Podsumowanie≠ | The Multi-layer Perceptron (MLP) is a feedforward neural network architecture trained by backpropagation, formalised by Rumelhart, Hinton, and Williams in their landmark 1986 Nature paper. Composed of an input layer, one or more hidden layers of neurons with nonlinear activation functions, and an output layer, the MLP can approximate any continuous function to arbitrary accuracy and serves as the conceptual bridge between classical machine learning and modern deep learning. | 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. |
| ScholarGateZbiór danych ↗ |
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