Linganisha mbinu

	Multilayer Perceptron yenye usimamizi nusu	Mtandao wa Tabaka Nyingi Wenye Usimamizi Dhaifu
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2006–2013	2016–2018
Mwanzilishi	Chapelle, O.; Scholkopf, B.; Zien, A. (eds.); Lee, D.-H.	Multiple contributors; paradigm formalized by Zhou (2018) and Ratner et al. (2016)
Aina	Semi-supervised feedforward neural network	Feedforward neural network trained under weak supervision
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	4	5
Muhtasari	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.