THEORETICAL FOUNDATIONS AND EXPERIMENTAL RESULTS FOR A HIERARCHICAL CLASSIFIER WITH OVERLAPPING CLUSTERS

DOI10.1111/j.1467-8640.2012.00469.xzbMath1274.68345OpenAlexW2011295323MaRDI QIDQ2857293

Adam Roman, Igor T. Podolak

Publication date: 1 November 2013

Published in: Computational Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-8640.2012.00469.x

zbMATH Keywords

hierarchical clusteringhierarchical classifierweak classifierclassification framework

Mathematics Subject Classification ID

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

Uses Software

• UCI-ml
• PRMLT

Cites Work

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• Bagging predictors
• A survey of hierarchical classification across different application domains
• Efficient algorithms for agglomerative hierarchical clustering methods
• A decision-theoretic generalization of on-line learning and an application to boosting
• A divide-and-conquer method for multi-net classifiers
• Hierarchical fusion of multiple classifiers for hyperspectral data analysis
• Improved boosting algorithms using confidence-rated predictions
• Feedforward Neural Network Construction Using Cross Validation
• Cryptographic limitations on learning Boolean formulae and finite automata
• The elements of statistical learning. Data mining, inference, and prediction
• Random forests

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