Table of Contents

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   Book Overview
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   Chapter 1 Data Dependence in Combining Classifiers
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   Chapter 2 Boosting with Averaged Weight Vectors
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   Chapter 3 Error Bounds for Aggressive and Conservative AdaBoost
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   Chapter 4 An Empirical Comparison of Three Boosting Algorithms on Real Data Sets with Artificial Class Noise
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   Chapter 5 Multiple Classifier Systems
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   Chapter 6 The Behavior Knowledge Space Fusion Method: Analysis of Generalization Error and Strategies for Performance Improvement
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   Chapter 7 Reducing the Overconfidence of Base Classifiers when Combining Their Decisions
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   Chapter 8 Linear Combiners for Classifier Fusion: Some Theoretical and Experimental Results
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    Chapter 9 Comparison of Classifier Selection Methods for Improving Committee Performance
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    Chapter 10 Towards Automated Classifier Combination for Pattern Recognition
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    Chapter 11 Serial Multiple Classifier Systems Exploiting a Coarse to Fine Output Coding
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    Chapter 12 Polychotomous Classification with Pairwise Classifiers: A New Voting Principle
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    Chapter 13 Multi-category Classification by Soft-Max Combination of Binary Classifiers
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    Chapter 14 A Sequential Scheduling Approach to Combining Multiple Object Classifiers Using Cross-Entropy
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    Chapter 15 Binary Classifier Fusion Based on the Basic Decomposition Methods
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    Chapter 16 Good Error Correcting Output Codes for Adaptive Multiclass Learning
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    Chapter 17 Finding Natural Clusters Using Multi-clusterer Combiner Based on Shared Nearest Neighbors
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    Chapter 18 An Ensemble Approach for Data Fusion with Learn++
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    Chapter 19 The Practical Performance Characteristics of Tomographically Filtered Multiple Classifier Fusion
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    Chapter 20 Accumulated-Recognition-Rate Normalization for Combining Multiple On/Off-Line Japanese Character Classifiers Tested on a Large Database
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    Chapter 21 Beam Search Extraction and Forgetting Strategies on Shared Ensembles
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    Chapter 22 A Markov Chain Approach to Multiple Classifier Fusion
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    Chapter 23 A Study of Ensemble of Hybrid Networks with Strong Regularization
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    Chapter 24 Multiple Classifier Systems
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    Chapter 25 Neural Net Ensembles for Lithology Recognition
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    Chapter 26 Improving Performance of a Multiple Classifier System Using Self-generating Neural Networks
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    Chapter 27 Negative Correlation Learning and the Ambiguity Family of Ensemble Methods
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    Chapter 28 Spectral Coefficients and Classifier Correlation
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    Chapter 29 Ensemble Construction via Designed Output Distortion
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    Chapter 30 Simulating Classifier Outputs for Evaluating Parallel Combination Methods
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    Chapter 31 A New Ensemble Diversity Measure Applied to Thinning Ensembles
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    Chapter 32 Ensemble Methods for Noise Elimination in Classification Problems
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    Chapter 33 New Boosting Algorithms for Classification Problems with Large Number of Classes Applied to a Handwritten Word Recognition Task
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    Chapter 34 Automatic Target Recognition Using Multiple Description Coding Models for Multiple Classifier Systems
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    Chapter 35 A Modular Multiple Classifier System for the Detection of Intrusions in Computer Networks
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    Chapter 36 Input Space Transformations for Multi-classifier Systems Based on n-tuple Classifiers with Application to Handwriting Recognition
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    Chapter 37 Building Classifier Ensembles for Automatic Sports Classification
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    Chapter 38 Classification of Aircraft Maneuvers for Fault Detection
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    Chapter 39 Solving Problems Two at a Time: Classification of Web Pages Using a Generic Pair-Wise Multiple Classifier System
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    Chapter 40 Design and Evaluation of an Adaptive Combination Framework for OCR Result Strings