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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≠ | 機械学習 | 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≠ | Feedforward neural network (supervised learning) | 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, artificial neural network | RNN, Elman network, Jordan network, simple recurrent network |
| 関連≠ | 4 | 3 |
| 概要≠ | 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. | 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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