Multiple Classifier Systems

Table of Contents

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   Book Overview
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   Chapter 1 Ensemble Methods for Tracking and Segmentation (Abstract)
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   Chapter 2 Ensembles and Multiple Classifiers: A Game-Theoretic View
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   Chapter 3 Anomaly Detection Using Ensembles
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   Chapter 4 Learning to Rank with Nonlinear Monotonic Ensemble
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   Chapter 5 A Bayesian Approach for Combining Ensembles of GP Classifiers
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   Chapter 6 Multiple Classifiers for Graph of Words Embedding
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   Chapter 7 A Dynamic Logistic Multiple Classifier System for Online Classification
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   Chapter 8 Ensemble Methods for Reinforcement Learning with Function Approximation
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    Chapter 9 GRASP Forest: A New Ensemble Method for Trees
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    Chapter 10 Ensembles of Decision Trees for Imbalanced Data
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    Chapter 11 Compact Ensemble Trees for Imbalanced Data
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    Chapter 12 Multiple Classifier Systems
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    Chapter 13 Approximate Convex Hulls Family for One-Class Classification
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    Chapter 14 Generalized Augmentation of Multiple Kernels
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    Chapter 15 Multiple Classifier Systems
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    Chapter 16 Multiple Classifier Systems
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    Chapter 17 Multiple Classifier Systems
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    Chapter 18 Dynamic Ensemble Selection for Off-Line Signature Verification
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    Chapter 19 Classifier Selection Approaches for Multi-label Problems
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    Chapter 20 Selection Strategies for pAUC-Based Combination of Dichotomizers
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    Chapter 21 Sequential Classifier Combination for Pattern Recognition in Wireless Sensor Networks
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    Chapter 22 Multi-class Multi-scale Stacked Sequential Learning
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    Chapter 23 Multiple Classifier Systems
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    Chapter 24 Two Stage Reject Rule for ECOC Classification Systems
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    Chapter 25 Introducing the Separability Matrix for Error Correcting Output Codes Coding
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    Chapter 26 Improving Accuracy and Speed of Optimum-Path Forest Classifier Using Combination of Disjoint Training Subsets
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    Chapter 27 Analyzing the Relationship between Diversity and Evidential Fusion Accuracy
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    Chapter 28 Classification by Cluster Analysis: A New Meta-Learning Based Approach
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    Chapter 29 C 3E: A Framework for Combining Ensembles of Classifiers and Clusterers
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    Chapter 30 A Latent Variable Pairwise Classification Model of a Clustering Ensemble
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    Chapter 31 CLOOSTING: CLustering Data with bOOSTING
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    Chapter 32 A Geometric Approach to Face Detector Combining
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    Chapter 33 Increase the Security of Multibiometric Systems by Incorporating a Spoofing Detection Algorithm in the Fusion Mechanism
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    Chapter 34 Cohort Based Approach to Multiexpert Class Verification
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    Chapter 35 A Modular Architecture for the Analysis of HTTP Payloads Based on Multiple Classifiers
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    Chapter 36 Incremental Boolean Combination of Classifiers
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    Chapter 37 Bagging Classifiers for Fighting Poisoning Attacks in Adversarial Classification Tasks
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    Chapter 38 Using a Behaviour Knowledge Space Approach for Detecting Unknown IP Traffic Flows