Multiple Classifier Systems

Table of Contents

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   Book Overview
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   Chapter 1 Combining Pattern Recognition Modalities at the Sensor Level Via Kernel Fusion
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   Chapter 2 The Neutral Point Method for Kernel-Based Combination of Disjoint Training Data in Multi-modal Pattern Recognition
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   Chapter 3 Kernel Combination Versus Classifier Combination
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   Chapter 4 Deriving the Kernel from Training Data
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   Chapter 5 On the Application of SVM-Ensembles Based on Adapted Random Subspace Sampling for Automatic Classification of NMR Data
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   Chapter 6 A New HMM-Based Ensemble Generation Method for Numeral Recognition
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   Chapter 7 Classifiers Fusion in Recognition of Wheat Varieties
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   Chapter 8 Multiple Classifier Methods for Offline Handwritten Text Line Recognition
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    Chapter 9 Applying Data Fusion Methods to Passage Retrieval in QAS
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    Chapter 10 A Co-training Approach for Time Series Prediction with Missing Data
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    Chapter 11 An Improved Random Subspace Method and Its Application to EEG Signal Classification
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    Chapter 12 Ensemble Learning Methods for Classifying EEG Signals
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    Chapter 13 Confidence Based Gating of Colour Features for Face Authentication
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    Chapter 14 View-Based Eigenspaces with Mixture of Experts for View-Independent Face Recognition
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    Chapter 15 Fusion of Support Vector Classifiers for Parallel Gabor Methods Applied to Face Verification
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    Chapter 16 Serial Fusion of Fingerprint and Face Matchers
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    Chapter 17 Boosting Lite – Handling Larger Datasets and Slower Base Classifiers
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    Chapter 18 Information Theoretic Combination of Classifiers with Application to AdaBoost
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    Chapter 19 Interactive Boosting for Image Classification
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    Chapter 20 Group-Induced Vector Spaces
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    Chapter 21 Selecting Diversifying Heuristics for Cluster Ensembles
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    Chapter 22 Unsupervised Texture Segmentation Using Multiple Segmenters Strategy
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    Chapter 23 Classifier Ensembles for Vector Space Embedding of Graphs
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    Chapter 24 Cascading for Nominal Data
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    Chapter 25 A Combination of Sample Subsets and Feature Subsets in One-Against-Other Classifiers
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    Chapter 26 Random Feature Subset Selection for Ensemble Based Classification of Data with Missing Features
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    Chapter 27 Feature Subspace Ensembles: A Parallel Classifier Combination Scheme Using Feature Selection
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    Chapter 28 Stopping Criteria for Ensemble-Based Feature Selection
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    Chapter 29 On Rejecting Unreliably Classified Patterns
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    Chapter 30 Bayesian Analysis of Linear Combiners
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    Chapter 31 Applying Pairwise Fusion Matrix on Fusion Functions for Classifier Combination
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    Chapter 32 Modelling Multiple-Classifier Relationships Using Bayesian Belief Networks
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    Chapter 33 Classifier Combining Rules Under Independence Assumptions
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    Chapter 34 Embedding Reject Option in ECOC Through LDPC Codes
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    Chapter 35 On Combination of Face Authentication Experts by a Mixture of Quality Dependent Fusion Classifiers
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    Chapter 36 Index Driven Combination of Multiple Biometric Experts for AUC Maximisation
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    Chapter 37 Q  −  stack : Uni- and Multimodal Classifier Stacking with Quality Measures
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    Chapter 38 Reliability-Based Voting Schemes Using Modality-Independent Features in Multi-classifier Biometric Authentication
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    Chapter 39 Optimal Classifier Combination Rules for Verification and Identification Systems
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    Chapter 40 Exploiting Diversity in Ensembles: Improving the Performance on Unbalanced Datasets
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    Chapter 41 On the Diversity-Performance Relationship for Majority Voting in Classifier Ensembles
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    Chapter 42 Hierarchical Behavior Knowledge Space
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    Chapter 43 A New Dynamic Ensemble Selection Method for Numeral Recognition
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    Chapter 44 Ensemble Learning in Linearly Combined Classifiers Via Negative Correlation
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    Chapter 45 Naïve Bayes Ensembles with a Random Oracle
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    Chapter 46 An Experimental Study on Rotation Forest Ensembles
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    Chapter 47 Cooperative Coevolutionary Ensemble Learning
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    Chapter 48 Robust Inference in Bayesian Networks with Application to Gene Expression Temporal Data
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    Chapter 49 An Ensemble Approach for Incremental Learning in Nonstationary Environments
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    Chapter 50 Multiple Classifier Systems in Remote Sensing: From Basics to Recent Developments
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    Chapter 51 Biometric Person Authentication Is a Multiple Classifier Problem