Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Selv-supervisert LightGBM× | Gradient Boosting× | |
|---|---|---|
| Fagfelt | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning |
| Opprinnelsesår≠ | 2017–2020 | 2001 |
| Opphavsperson≠ | Ke, G. et al. (LightGBM); self-supervised paradigm adapted from broader SSL literature | Friedman, J. H. |
| Type≠ | Hybrid (self-supervised pretraining + gradient boosting) | Ensemble (sequential boosting of decision trees) |
| Opprinnelig kilde≠ | 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, 30. link ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Alias | SSL-LightGBM, self-supervised gradient boosting, pretraining LightGBM, pseudo-label LightGBM | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Relaterte≠ | 6 | 5 |
| Sammendrag≠ | Self-supervised LightGBM combines the self-supervised learning paradigm with the LightGBM gradient boosting framework to exploit large volumes of unlabeled tabular data. A self-supervised pretext task — such as masked feature prediction or contrastive corruption — generates rich feature representations or pseudo-labels that are then used to train or fine-tune a LightGBM model, substantially improving performance in label-scarce regimes. | Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost. |
| ScholarGateDatasett ↗ |
|
|