Multiclass Boosting Algorithms for Shrinkage Estimators of Class Probability

DOI10.1007/978-3-540-75225-7_29zbMath1142.68396OpenAlexW2104006586MaRDI QIDQ3520072

Takafumi Kanamori

Publication date: 19 August 2008

Published in: Lecture Notes in Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-540-75225-7_29

Mathematics Subject Classification ID

Computational learning theory (68Q32) Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• rpart
• Kernlab
• mlbench
• AdaBoost.MH

Cites Work

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• A decision-theoretic generalization of on-line learning and an application to boosting
• Inference for the generalization error
• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• Statistical behavior and consistency of classification methods based on convex risk minimization.
• Support-vector networks
• Convexity, Classification, and Risk Bounds
• Soft margins for AdaBoost

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