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2006 NIPS NeurIPS 2006

AdaBoost is Consistent

Abstract

The risk, or probability of error, of the classifier produced by the AdaBoost algorithm is investigated. In particular, we consider the stopping strategy to be used in AdaBoost to achieve universal consistency. We show that provided AdaBoost is stopped after n iterations--for sample size n and < 1--the sequence of risks of the classifiers it produces approaches the Bayes risk if Bayes risk L > 0.

🚀 Conference Pioneer — NIPS 2006
🧭 Keyword Pioneer — adaboost
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization
📈 Trend Setter — Ensemble Methods
🐣 Hot Topic Early Bird — statistical learning

Authors

Peter L. Bartlett , Mikhail Traskin

Topics

Machine Learning > Core Methods > Classification Machine Learning > Optimization & Theory > Learning Theory Machine Learning > Optimization & Theory > Statistical Learning Machine Learning > Optimization & Theory > Theory Machine Learning > Core Methods > Ensemble Methods Machine Learning > Learning Types > Classification

Keywords

statistical learning learning theory statistical consistency ensemble learning adaboost consistency analysis risk minimization universal consistency boosting algorithm stopping criterion bayes risk boosting consistency
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