Boosting Ensemble
Boosting is an ensemble method that sequentially trains weak learners and combines them into a strong predictor by focusing on samples that previous models misclassified. Each new weak learner is weighted according to the difficulty of its training task, and final predictions are made via weighted voting. Pioneered by Schapire (1990) and refined in AdaBoost (Freund & Schapire, 1997), boosting converts weak learners (barely better than random) into strong learners through sequential reweighting.
Rekod sumber
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- Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197-227. · DOI 10.1023/A:1022648800760
- 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
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