Vote counting measures for ensemble classifiers.

DOI10.1016/S0031-3203(03)00191-2zbMath1059.68119MaRDI QIDQ1425964

Terry Windeatt

Publication date: 14 March 2004

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

zbMATH Keywords

Error-correctingEnsemblesBinary codingDecision level fusionMultiple classifiers

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10)

Related Items (1)

Consensus analysis of multiple classifiers using non-repetitive variables: diagnostic application to microarray gene expression data

Uses Software

• UCI-ml

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Bagging predictors
• Modeling by shortest data description
• The computation of complete and reduced sets of orthogonal spectral coefficients for logic design and pattern recognition purposes
• A decision-theoretic generalization of on-line learning and an application to boosting
• Variance and bias for general loss functions
• Arcing classifiers. (With discussion)
• 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)
• Empirical margin distributions and bounding the generalization error of combined classifiers
• Recursive partitioning technique for combining multiple classifiers
• Rademacher penalties and structural risk minimization
• Soft margins for AdaBoost

This page was built for publication: Vote counting measures for ensemble classifiers.