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| 多層パーセプトロン (MLP)× | リカレントニューラルネットワーク (RNN)× | |
|---|---|---|
| 分野 | 深層学習 | 深層学習 |
| 系統 | Machine learning | Machine learning |
| 提唱年≠ | 1986 | 1986–1990 |
| 提唱者≠ | Rumelhart, D. E.; Hinton, G. E.; Williams, R. J. | Rumelhart, D. E.; Elman, J. L. |
| 種類≠ | Supervised feedforward neural network | Sequential neural network |
| 原典≠ | Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536. DOI ↗ | Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI ↗ |
| 別名≠ | MLP, feedforward neural network, fully connected neural network, vanilla neural network | RNN, Elman network, Jordan network, simple recurrent network |
| 関連≠ | 4 | 3 |
| 概要≠ | 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. | 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. |
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