Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Pašuzraudzīta gradienta pastiprināšana× | LightGBM× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2020s | 2017 |
| Autors≠ | Various researchers (Zhang et al. and others) | Ke, G. et al. (Microsoft) |
| Tips≠ | Ensemble (self-supervised + gradient boosting) | Gradient boosting decision tree ensemble |
| Pirmavots≠ | Zhang, Y., Zhang, J., & Yang, Q. (2022). Self-Supervised Gradient Boosting for Semi-Supervised Learning on Tabular Data. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. link ↗ | Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q. & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems (NeurIPS) 30, 3146–3154. link ↗ |
| Citi nosaukumi | SSL gradient boosting, self-supervised boosting, semi-supervised gradient boosting, SSL-GBM | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Self-supervised gradient boosting extends the classic gradient boosting framework by incorporating self-supervised pretext tasks to exploit unlabeled data. The model first learns useful feature representations from unannotated samples, then uses those representations to guide the sequential ensemble of weak learners, achieving strong predictive performance even when labeled examples are scarce. | LightGBM is Microsoft's gradient boosting decision tree implementation, introduced by Ke and colleagues in 2017, that grows trees leaf-wise and bins features into histograms for speed. On large datasets it is much faster than XGBoost while retaining strong predictive accuracy. |
| ScholarGateDatu kopa ↗ |
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