手法を比較

	弱教師あり多層パーセプトロン	多層パーセプトロン (MLP)
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2016–2018	1986
提唱者	Multiple contributors; paradigm formalized by Zhou (2018) and Ratner et al. (2016)	Rumelhart, D. E.; Hinton, G. E.; Williams, R. J.
種類	Feedforward neural network trained under weak supervision	Supervised feedforward neural network
原典	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.
別名	WS-MLP, weakly supervised feedforward network, noisy-label MLP, weak-label multilayer perceptron	MLP, feedforward neural network, fully connected neural network, vanilla neural network
関連	5	4
概要	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.