ScholarGate
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Machine learningMachine learning

Boosting

Boosting on järjestikune ansamblitehnika, mis muudab paljud lihtsad, vaevu juhuslikust paremad õppijad üheks kõrge täpsusega mudeliks, keskendudes korduvalt nendele näidetele, milles eelmised õppijad eksisid, ning seejärel kombineerides kõik õppijad nende individuaalse täpsusega proportsionaalselt kaalutult.

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Allikad

  1. Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. DOI: 10.1006/jcss.1997.1504
  2. Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197–227. DOI: 10.1007/BF00116037

Kuidas sellele lehele viidata

ScholarGate. (2026, June 3). Boosting (Ensemble of Sequentially Weighted Weak Learners). ScholarGate. https://scholargate.app/et/machine-learning/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.

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Sellele viitavad

ScholarGateBoosting (Boosting (Ensemble of Sequentially Weighted Weak Learners)). Loetud 2026-06-15 aadressilt https://scholargate.app/et/machine-learning/boosting · Andmestik: https://doi.org/10.5281/zenodo.20539026