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Active Learning Gradient Boosting

Active Learning Gradient Boosting kombinerer den kraftfulde forudsigelsesmæssige nøjagtighed af gradient boosted trees med en aktiv lærings-loop, der udvælger de mest informative umærkede eksempler til menneskelig annotering. Ved kun at forespørge de instanser, modellen er mest usikker på, opnår metoden høj nøjagtighed med langt færre mærkede eksempler end passiv superviseret læring.

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Kilder

  1. Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison. link
  2. Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI: 10.1214/aos/1013203451

Sådan citerer du denne side

ScholarGate. (2026, June 3). Active Learning with Gradient Boosting (Query-by-Committee / Uncertainty Sampling with Gradient Boosted Trees). ScholarGate. https://scholargate.app/da/machine-learning/active-learning-gradient-boosting

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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.

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ScholarGateActive Learning Gradient Boosting (Active Learning with Gradient Boosting (Query-by-Committee / Uncertainty Sampling with Gradient Boosted Trees)). Hentet 2026-06-15 fra https://scholargate.app/da/machine-learning/active-learning-gradient-boosting · Datasæt: https://doi.org/10.5281/zenodo.20539026