Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| LSTM iliyo na Usimamizi dhaifu× | LSTM nusu-simamizi× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Kina | Ujifunzaji wa Kina |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2016–2018 | 2015–2018 |
| Mwanzilishi≠ | Ratner et al. (data programming framework); Hochreiter & Schmidhuber (LSTM backbone) | Hochreiter, S. & Schmidhuber, J. (LSTM); semi-supervised extensions by various authors (2015–2020) |
| Aina≠ | Weakly supervised sequence model | Semi-supervised sequence model |
| Chanzo asilia≠ | 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. link ↗ | Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. DOI ↗ |
| Majina mbadala | WS-LSTM, noisy-label LSTM, distant-supervision LSTM, data-programming LSTM | SSL-LSTM, semi-supervised sequence model, LSTM with unlabeled data, pseudo-label LSTM |
| Zinazohusiana≠ | 6 | 3 |
| Muhtasari≠ | 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. | 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. |
| ScholarGateSeti ya data ↗ |
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