Vertaile menetelmiä
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| Monikerki-kerrosperceptron (MLP)× | XGBoost× | |
|---|---|---|
| Tieteenala≠ | Syväoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 1986 | 2016 |
| Kehittäjä≠ | Rumelhart, D. E.; Hinton, G. E.; Williams, R. J. | Chen, T. & Guestrin, C. |
| Tyyppi≠ | Supervised feedforward neural network | Ensemble (gradient-boosted decision trees) |
| Alkuperäislähde≠ | 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 ↗ |
| Rinnakkaisnimet≠ | MLP, feedforward neural network, fully connected neural network, vanilla neural network | XGBoost, extreme gradient boosting, scalable tree boosting |
| Liittyvät≠ | 4 | 5 |
| Tiivistelmä≠ | A Multilayer Perceptron is a classic fully connected feedforward neural network trained with the backpropagation algorithm, as formalised by Rumelhart, Hinton & Williams in their landmark 1986 Nature paper. Composed of an input layer, one or more hidden layers of neurons, and an output layer, the MLP learns nonlinear mappings from input features to target outputs and serves as the foundational building block of 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. |
| ScholarGateAineisto ↗ |
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