Zapis dokaza metode
Boosting
Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.
Izvorni zapis
Citati kopirani doslovno iz izvornog zapisa metode. Ne impliciraju nikakvu provjeru na razini tvrdnje.
Boosting (Ensemble of Sequentially Weighted Weak Learners)
Taksonomski zapis metode · ml-model / machine-learning
- 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
- Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197–227. · DOI 10.1007/BF00116037
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Povezane metode
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