Semi-supervised Learning with Light Gradient Boosting Machine
Semi-supervised LightGBM kombinerer LightGBM's yderst effektive gradient boosting-framework med semi-supervised strategier — oftest pseudo-labeling eller self-training — for at udnytte store mængder umærkede data sammen med et mindre sæt mærkede data, hvilket forbedrer prædiktiv ydeevne, når det er dyrt eller tidskrævende at opnå mærker.
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Method map
The neighbourhood of related methods — select a node to explore.
Kilder
- 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, 3146–3154. link ↗
- Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Sådan citerer du denne side
ScholarGate. (2026, June 3). Semi-supervised Learning with Light Gradient Boosting Machine. ScholarGate. https://scholargate.app/da/machine-learning/semi-supervised-lightgbm
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- LightGBMMaskinlæring↔ compare
- Semi-overvåget Gradient BoostingMaskinlæring↔ compare
- Semi-supervised Random ForestMaskinlæring↔ compare
- Semi-supervised XGBoostMaskinlæring↔ compare
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