Machine learningMachine learning

Glasački ansambl

Glasački ansambl obučava nekoliko raznovrsnih klasifikatora nezavisno i kombinuje njihova predviđanja glasanjem: „tvrdo glasanje“ (hard voting) bira klasu koju je odabrao najveći broj modela, dok „meko glasanje“ (soft voting) usrednjava njihove procene verovatnoće klase, opciono sa težinama po modelu. Kombinacija obično nadmašuje bilo kog pojedinačnog člana i ne zahteva dodatnu obuku nakon što su osnovni modeli podešeni.

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Izvori

  1. Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
  2. Dietterich, T. G. (2000). Ensemble Methods in Machine Learning. In J. Kittler & F. Roli (Eds.), Multiple Classifier Systems (MCS 2000), Lecture Notes in Computer Science, vol 1857, pp. 1–15. Springer. DOI: 10.1007/3-540-45014-9_1

Kako citirati ovu stranicu

ScholarGate. (2026, June 3). Voting Ensemble (Majority and Weighted Voting of Multiple Classifiers). ScholarGate. https://scholargate.app/sr/machine-learning/voting-ensemble

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ScholarGateVoting Ensemble (Voting Ensemble (Majority and Weighted Voting of Multiple Classifiers)). Preuzeto 2026-06-15 sa https://scholargate.app/sr/machine-learning/voting-ensemble · Skup podataka: https://doi.org/10.5281/zenodo.20539026