Σύγκριση μεθόδων

	Εκπαίδευση LSTM με Ασθενή Επίβλεψη	Αναδρομικό Νευρωνικό Δίκτυο
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2016–2018	1986–1990
Δημιουργός	Ratner et al. (data programming framework); Hochreiter & Schmidhuber (LSTM backbone)	Rumelhart, D. E.; Elman, J. L.
Τύπος	Weakly supervised sequence model	Sequential neural network
Θεμελιώδης πηγή	Ratner, A., De Sa, C., Wu, S., Selsam, D., & Re, C. (2016). Data Programming: Creating Large Training Sets, Quickly. Advances in Neural Information Processing Systems (NeurIPS), 29.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
Εναλλακτικές ονομασίες	WS-LSTM, noisy-label LSTM, distant-supervision LSTM, data-programming LSTM	RNN, Elman network, Jordan network, simple recurrent network
Συναφείς	6	3
Σύνοψη	Weakly supervised LSTM trains a Long Short-Term Memory network on sequence data where clean, manually annotated labels are scarce or absent. Instead, multiple imperfect label sources — heuristic rules, distant supervision, crowdsourcing, or programmatic labeling functions — are combined to produce probabilistic training labels, which are then used to supervise the LSTM. This allows scalable training on large unlabeled corpora without exhaustive human annotation.	A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.