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| Schwache überwachtes Multilayer Perceptron× | Semi-supervised Multilayer Perceptron× | |
|---|---|---|
| Fachgebiet | Deep Learning | Deep Learning |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2016–2018 | 2006–2013 |
| Urheber≠ | Multiple contributors; paradigm formalized by Zhou (2018) and Ratner et al. (2016) | Chapelle, O.; Scholkopf, B.; Zien, A. (eds.); Lee, D.-H. |
| Typ≠ | Feedforward neural network trained under weak supervision | Semi-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 ↗ | Chapelle, O., Scholkopf, B. & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Aliasnamen | WS-MLP, weakly supervised feedforward network, noisy-label MLP, weak-label multilayer perceptron | SSL-MLP, semi-supervised MLP, semi-supervised feedforward network, partially supervised multilayer perceptron |
| 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 semi-supervised multilayer perceptron (SSL-MLP) is a feedforward neural network trained on a small pool of labeled examples together with a larger pool of unlabeled examples. By combining supervised cross-entropy loss on labeled data with an unsupervised consistency or pseudo-label objective on unlabeled data, it extracts far more signal from the data than a purely supervised MLP trained on labels alone. |
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