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Applications of Supervised and Unsupervised Ensemble Methods

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Cover of 'Applications of Supervised and Unsupervised Ensemble Methods'

Table of Contents

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    Book Overview
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    Chapter 1 An Ensemble Pruning Primer
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    Chapter 2 Evade Hard Multiple Classifier Systems
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    Chapter 3 A Personal Antispam System Based on a Behaviour-Knowledge Space Approach
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    Chapter 4 Applications of Supervised and Unsupervised Ensemble Methods
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    Chapter 5 Prediction of Gene Function Using Ensembles of SVMs and Heterogeneous Data Sources
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    Chapter 6 Partitioner Trees for Classification: A New Ensemble Method
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    Chapter 7 Disturbing Neighbors Diversity for Decision Forests
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    Chapter 8 Improving Supervised Learning with Multiple Clusterings
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    Chapter 9 The Neighbors Voting Algorithm and Its Applications
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    Chapter 10 Clustering Ensembles with Active Constraints
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    Chapter 11 Verifiable Ensembles of Low-Dimensional Submodels for Multi-class Problems with Imbalanced Misclassification Costs
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    Chapter 12 Independent Data Model Selection for Ensemble Dispersion Forecasting
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    Chapter 13 Integrating Liknon Feature Selection and Committee Training
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    Chapter 14 Evaluating Hybrid Ensembles for Intelligent Decision Support for Intensive Care
Attention for Chapter 11: Verifiable Ensembles of Low-Dimensional Submodels for Multi-class Problems with Imbalanced Misclassification Costs
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Chapter title
Verifiable Ensembles of Low-Dimensional Submodels for Multi-class Problems with Imbalanced Misclassification Costs
Chapter number 11
Book title
Applications of Supervised and Unsupervised Ensemble Methods
Published by
Springer, Berlin, Heidelberg, January 2009
DOI 10.1007/978-3-642-03999-7_11
Book ISBNs
978-3-64-203998-0, 978-3-64-203999-7
Authors

Sebastian Nusser, Clemens Otte, Werner Hauptmann

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