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| Schwache überwachtes Multilayer Perceptron× | Mehrschichtiges Perzeptron (MLP)× | |
|---|---|---|
| Fachgebiet | Deep Learning | Deep Learning |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2016–2018 | 1986 |
| Urheber≠ | Multiple contributors; paradigm formalized by Zhou (2018) and Ratner et al. (2016) | Rumelhart, D. E.; Hinton, G. E.; Williams, R. J. |
| Typ≠ | Feedforward neural network trained under weak supervision | Supervised feedforward neural network |
| Wegweisende Quelle≠ | Zhou, Z.-H. (2018). A brief introduction to weakly supervised learning. National Science Review, 5(1), 44–53. DOI ↗ | Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536. DOI ↗ |
| Aliasnamen≠ | WS-MLP, weakly supervised feedforward network, noisy-label MLP, weak-label multilayer perceptron | MLP, feedforward neural network, fully connected neural network, vanilla neural network |
| Verwandt≠ | 5 | 4 |
| Zusammenfassung≠ | A Weakly Supervised Multilayer Perceptron trains a standard feedforward neural network when only imperfect supervision is available — labels may be noisy, incomplete, crowd-sourced, rule-generated, or derived from distant supervision — enabling learning at scale without the cost of full expert annotation. | 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. |
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