Robust Boosting Algorithm Against Mislabeling in Multiclass Problems

DOI10.1162/neco.2007.11-06-400zbMath1140.68481OpenAlexW1968037646WikidataQ51896823 ScholiaQ51896823MaRDI QIDQ3503733

Takafumi Kanamori, Shinto Eguchi, Takashi Takenouchi, Noboru Murata

Publication date: 9 June 2008

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/neco.2007.11-06-400

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (7)

Binary classification with a pseudo exponential model and its application for multi-task learning ⋮ Learning kernel logistic regression in the presence of class label noise ⋮ Geometry of EM and related iterative algorithms ⋮ Entropy and divergence associated with power function and the statistical application ⋮ An Estimation of Generalized Bradley-Terry Models Based on the em Algorithm ⋮ Deformation of log-likelihood loss function for multiclass boosting ⋮ An Extension of the Receiver Operating Characteristic Curve and AUC-Optimal Classification

Cites Work

• A decision-theoretic generalization of on-line learning and an application to boosting
• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• Improved boosting algorithms using confidence-rated predictions
• 10.1162/15324430260185628
• A class of logistic-type discriminant functions
• Robustifying AdaBoost by Adding the Naive Error Rate
• Information Geometry of U-Boost and Bregman Divergence
• Convexity, Classification, and Risk Bounds
• Soft margins for AdaBoost

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