So sánh phương pháp

	Mạng perceptron đa lớp bán giám sát	LSTM bán giám sát
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2006–2013	2015–2018
Người khởi xướng	Chapelle, O.; Scholkopf, B.; Zien, A. (eds.); Lee, D.-H.	Hochreiter, S. & Schmidhuber, J. (LSTM); semi-supervised extensions by various authors (2015–2020)
Loại	Semi-supervised feedforward neural network	Semi-supervised sequence model
Công trình gốc	Chapelle, O., Scholkopf, B. & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9	Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.
Tên gọi khác	SSL-MLP, semi-supervised MLP, semi-supervised feedforward network, partially supervised multilayer perceptron	SSL-LSTM, semi-supervised sequence model, LSTM with unlabeled data, pseudo-label LSTM
Liên quan	4	3
Tóm tắt	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.	Semi-supervised LSTM combines the sequential memory of Long Short-Term Memory networks with semi-supervised learning strategies — using a small labeled dataset alongside a large pool of unlabeled sequences. The model is pretrained or regularized on unlabeled data, then fine-tuned on labeled examples, delivering strong generalization when labeled data is scarce.