Tutorial series on brain-inspired computing. VI: Geometrical structure of boosting algorithm

DOI10.1007/s00354-006-0006-0zbMath1119.68385OpenAlexW2472201579MaRDI QIDQ857990

Noboru Murata, Takashi Takenouchi, Takafumi Kanamori

Publication date: 5 January 2007

Published in: New Generation Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00354-006-0006-0

zbMATH Keywords

Boosting, classification problem, large-scale learning machine, statistical learning theory

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• AdaBoost.MH

Cites Work

• Unnamed Item
• A decision-theoretic generalization of on-line learning and an application to boosting
• Boosting the margin: a new explanation for the effectiveness of voting methods
• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• Robust Blind Source Separation by Beta Divergence
• Robustifying AdaBoost by Adding the Naive Error Rate
• Information Geometry of U-Boost and Bregman Divergence
• Convexity, Classification, and Risk Bounds

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