Multiple Classifier Systems

Table of Contents

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   Book Overview
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   Chapter 1 Semi-supervised Multiple Classifier Systems: Background and Research Directions
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   Chapter 2 Boosting GMM and Its Two Applications
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   Chapter 3 Boosting Soft-Margin SVM with Feature Selection for Pedestrian Detection
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   Chapter 4 Observations on Boosting Feature Selection
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   Chapter 5 Boosting Multiple Classifiers Constructed by Hybrid Discriminant Analysis
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   Chapter 6 Decoding Rules for Error Correcting Output Code Ensembles
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   Chapter 7 A Probability Model for Combining Ranks
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   Chapter 8 EER of Fixed and Trainable Fusion Classifiers: A Theoretical Study with Application to Biometric Authentication Tasks
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    Chapter 9 Mixture of Gaussian Processes for Combining Multiple Modalities
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    Chapter 10 Dynamic Classifier Integration Method
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    Chapter 11 Recursive ECOC for Microarray Data Classification
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    Chapter 12 Using Dempster-Shafer Theory in MCF Systems to Reject Samples
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    Chapter 13 Multiple Classifier Fusion Performance in Networked Stochastic Vector Quantisers
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    Chapter 14 On Deriving the Second-Stage Training Set for Trainable Combiners
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    Chapter 15 Using Independence Assumption to Improve Multimodal Biometric Fusion
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    Chapter 16 Half-Against-Half Multi-class Support Vector Machines
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    Chapter 17 Combining Feature Subsets in Feature Selection
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    Chapter 18 ACE: Adaptive Classifiers-Ensemble System for Concept-Drifting Environments
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    Chapter 19 Using Decision Tree Models and Diversity Measures in the Selection of Ensemble Classification Models
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    Chapter 20 Ensembles of Classifiers from Spatially Disjoint Data
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    Chapter 21 Optimising Two-Stage Recognition Systems
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    Chapter 22 Design of Multiple Classifier Systems for Time Series Data
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    Chapter 23 Ensemble Learning with Biased Classifiers: The Triskel Algorithm
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    Chapter 24 Cluster-Based Cumulative Ensembles
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    Chapter 25 Ensemble of SVMs for Incremental Learning
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    Chapter 26 Design of a New Classifier Simulator
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    Chapter 27 Evaluation of Diversity Measures for Binary Classifier Ensembles
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    Chapter 28 Which Is the Best Multiclass SVM Method? An Empirical Study
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    Chapter 29 Over-Fitting in Ensembles of Neural Network Classifiers Within ECOC Frameworks
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    Chapter 30 Between Two Extremes: Examining Decompositions of the Ensemble Objective Function
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    Chapter 31 Data Partitioning Evaluation Measures for Classifier Ensembles
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    Chapter 32 Dynamics of Variance Reduction in Bagging and Other Techniques Based on Randomisation
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    Chapter 33 Ensemble Confidence Estimates Posterior Probability
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    Chapter 34 Using Domain Knowledge in the Random Subspace Method: Application to the Classification of Biomedical Spectra
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    Chapter 35 An Abnormal ECG Beat Detection Approach for Long-Term Monitoring of Heart Patients Based on Hybrid Kernel Machine Ensemble
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    Chapter 36 Speaker Verification Using Adapted User-Dependent Multilevel Fusion
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    Chapter 37 Multi-modal Person Recognition for Vehicular Applications
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    Chapter 38 Using an Ensemble of Classifiers to Audit a Production Classifier
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    Chapter 39 Analysis and Modelling of Diversity Contribution to Ensemble-Based Texture Recognition Performance
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    Chapter 40 Combining Audio-Based and Video-Based Shot Classification Systems for News Videos Segmentation
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    Chapter 41 Designing Multiple Classifier Systems for Face Recognition
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    Chapter 42 Exploiting Class Hierarchies for Knowledge Transfer in Hyperspectral Data