Comparer des méthodes

	Perceptron multicouche faiblement supervisé	Perceptron multicouche (MLP)
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2016–2018	1986
Auteur d'origine	Multiple contributors; paradigm formalized by Zhou (2018) and Ratner et al. (2016)	Rumelhart, D. E.; Hinton, G. E.; Williams, R. J.
Type	Feedforward neural network trained under weak supervision	Supervised feedforward neural network
Source fondatrice	Zhou, Z.-H. (2018). A brief introduction to weakly supervised learning. National Science Review, 5(1), 44–53.	Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.
Alias	WS-MLP, weakly supervised feedforward network, noisy-label MLP, weak-label multilayer perceptron	MLP, feedforward neural network, fully connected neural network, vanilla neural network
Apparentées	5	4
Résumé	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.