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| Semi-overvåget Multilayer Perceptron× | Svagt Overvåget Multilayer Perceptron× | |
|---|---|---|
| Fagområde | Dyb læring | Dyb læring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 2006–2013 | 2016–2018 |
| Ophavsperson≠ | Chapelle, O.; Scholkopf, B.; Zien, A. (eds.); Lee, D.-H. | Multiple contributors; paradigm formalized by Zhou (2018) and Ratner et al. (2016) |
| Type≠ | Semi-supervised feedforward neural network | Feedforward neural network trained under weak supervision |
| Oprindelig kilde≠ | Chapelle, O., Scholkopf, B. & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 | Zhou, Z.-H. (2018). A brief introduction to weakly supervised learning. National Science Review, 5(1), 44–53. DOI ↗ |
| Aliasser | SSL-MLP, semi-supervised MLP, semi-supervised feedforward network, partially supervised multilayer perceptron | WS-MLP, weakly supervised feedforward network, noisy-label MLP, weak-label multilayer perceptron |
| Relaterede≠ | 4 | 5 |
| Resumé≠ | 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. | 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. |
| ScholarGateDatasæt ↗ |
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