Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Gradient Boosting× | Daudzpusīgā apguve× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2001 | 1970s–2006 (formalized) |
| Autors≠ | Friedman, J. H. | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| Tips≠ | Ensemble (sequential boosting of decision trees) | Learning paradigm |
| Pirmavots≠ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Citi nosaukumi | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. | Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained. |
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