Machine learningMachine learning

Boosting

Boosting je sekvencijalna ansambl tehnika koja pretvara mnoge jednostavne, jedva bolje od slučajnih, modele učenja u jedan visoko precizan model ponovnim fokusiranjem obuke na primjere koje su prethodni modeli pogrešno klasificirali, a zatim kombiniranjem svih modela s utezima proporcionalnim njihovoj pojedinačnoj točnosti.

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Izvori

  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

Kako citirati ovu stranicu

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

ScholarGateBoosting (Boosting (Ensemble of Sequentially Weighted Weak Learners)). Preuzeto 2026-06-15 s https://scholargate.app/hr/machine-learning/boosting · Skup podataka: https://doi.org/10.5281/zenodo.20539026