Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Daudzslāņu perceptrons (MLP)× | Random Forest× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1986 | 2001 |
| Autors≠ | Rumelhart, D. E., Hinton, G. E., & Williams, R. J. | Breiman, L. |
| Tips≠ | Feedforward neural network (supervised learning) | Ensemble (bagging of decision trees) |
| Pirmavots≠ | Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Citi nosaukumi≠ | MLP, feedforward neural network, fully connected neural network, artificial neural network | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Saistītās | 4 | 4 |
| Kopsavilkums≠ | 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. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. |
| ScholarGateDatu kopa ↗ |
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