Probability estimation for multi-class classification using adaboost

DOI10.1016/j.patcog.2014.06.008zbMath1373.68329OpenAlexW2058254336MaRDI QIDQ1677008

Lizuo Jin, Qingfeng Nie, Shu-Min Fei

Publication date: 10 November 2017

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2014.06.008

zbMATH Keywords

predictionBayesAdaBoostprobability estimation

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• AdaBoost.MH
• AdaBoost-SAMME

Cites Work

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• Logistic regression using covariates obtained by product-unit neural network models
• Generalized additive models
• Additive estimators for probabilities of correct classification
• A decision-theoretic generalization of on-line learning and an application to boosting
• Classification by pairwise coupling
• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• Boosting a weak learning algorithm by majority
• Improved boosting algorithms using confidence-rated predictions
• Logistic model trees
• Matrix theory. Basic results and techniques

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