Bandingkan metode
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| Multilayer Perceptron (MLP)× | Random Forest× | Jaringan Saraf Berulang (Recurrent Neural Network - RNN)× | |
|---|---|---|---|
| Bidang≠ | Pembelajaran Mendalam | Pembelajaran Mesin | Pembelajaran Mendalam |
| Keluarga | Machine learning | Machine learning | Machine learning |
| Tahun asal≠ | 1986 | 2001 | 1986–1990 |
| Pencetus≠ | Rumelhart, D. E.; Hinton, G. E.; Williams, R. J. | Breiman, L. | Rumelhart, D. E.; Elman, J. L. |
| Tipe≠ | Supervised feedforward neural network | Ensemble (bagging of decision trees) | Sequential neural network |
| Sumber perintis≠ | 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 ↗ | Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI ↗ |
| Alias≠ | MLP, feedforward neural network, fully connected neural network, vanilla neural network | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble | RNN, Elman network, Jordan network, simple recurrent network |
| Terkait≠ | 4 | 4 | 3 |
| Ringkasan≠ | 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. | 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. | A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models. |
| ScholarGateSet data ↗ |
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