Machine learningMachine learning
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.
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Sources
- 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 ↗
Related methods
Referenced by
Active learning BoostingActive learning Stacking ensembleActive Learning Voting EnsembleBayesian BaggingBayesian BoostingBayesian Stacking EnsembleEnsemble Active LearningEnsemble Apriori AlgorithmEnsemble Association RulesEnsemble Decision TreeEnsemble Federated LearningEnsemble Few-shot learningEnsemble Gaussian Mixture ModelEnsemble Logistic RegressionEnsemble Naive BayesEnsemble Online LearningEnsemble Semi-supervised LearningEnsemble Support Vector MachineEnsemble Transfer LearningOnline BoostingOnline Gradient BoostingRegularized BoostingRegularized Decision TreeRegularized Gradient BoostingRegularized Stacking EnsembleRobust BaggingRobust BoostingRobust Gradient BoostingRobust Stacking EnsembleRobust Voting EnsembleSelf-supervised BoostingSemi-supervised Gradient BoostingSemi-supervised Voting EnsembleVoting Ensemble