Machine learningMachine learning

Aktivno učenje s pojačanim gradijentom

Aktivno učenje s pojačanim gradijentom kombinira snažnu prediktivnu točnost pojačanih stabala s petljom aktivnog učenja koja odabire najinformativnije neoznačene primjere za ljudsku anotaciju. Upitima samo na instancama o kojima je model najneizvjesniji, metoda postiže visoku točnost s daleko manje označenih primjera nego pasivno nadzirano učenje.

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Method map

The neighbourhood of related methods — select a node to explore.

Aktivno učenje s pojačanim gradijentom
Aktivno učenjePovećanje gradijentaSlučajna šumaXGBoost

Izvori

  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

Kako citirati ovu stranicu

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

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.

Compare side by side
ScholarGateActive Learning Gradient Boosting (Active Learning with Gradient Boosting (Query-by-Committee / Uncertainty Sampling with Gradient Boosted Trees)). Preuzeto 2026-06-15 s https://scholargate.app/hr/machine-learning/active-learning-gradient-boosting · Skup podataka: https://doi.org/10.5281/zenodo.20539026