Multiple Classifier Systems

Invited Paper.- Data Dependence in Combining Classifiers.- Boosting.- Boosting with Averaged Weight Vectors.- Error Bounds for Aggressive and Conservative AdaBoost.- An Empirical Comparison of Three Boosting Algorithms on Real Data Sets with Artificial Class Noise.- The Beneficial Effects of Using Multi-net Systems That Focus on Hard Patterns.- Combination Rules.- The Behavior Knowledge Space Fusion Method: Analysis of Generalization Error and Strategies for Performance Improvement.- Reducing the Overconfidence of Base Classifiers when Combining Their Decisions.- Linear Combiners for Classifier Fusion: Some Theoretical and Experimental Results.- Comparison of Classifier Selection Methods for Improving Committee Performance.- Towards Automated Classifier Combination for Pattern Recognition.- Multi-class Methods.- Serial Multiple Classifier Systems Exploiting a Coarse to Fine Output Coding.- Polychotomous Classification with Pairwise Classifiers: A New Voting Principle.- Multi-category Classification by Soft-Max Combination of Binary Classifiers.- A Sequential Scheduling Approach to Combining Multiple Object Classifiers Using Cross-Entropy.- Binary Classifier Fusion Based on the Basic Decomposition Methods.- Fusion Schemes Architectures.- Good Error Correcting Output Codes for Adaptive Multiclass Learning.- Finding Natural Clusters Using Multi-clusterer Combiner Based on Shared Nearest Neighbors.- An Ensemble Approach for Data Fusion with Learn++.- The Practical Performance Characteristics of Tomographically Filtered Multiple Classifier Fusion.- Accumulated-Recognition-Rate Normalization for Combining Multiple On/Off-Line Japanese Character Classifiers Tested on a Large Database.- Beam Search Extraction and Forgetting Strategies on Shared Ensembles.- A Markov Chain Approach to Multiple Classifier Fusion.- Neural Network Ensembles.- A Study of Ensemble of Hybrid Networks with Strong Regularization.- Combining Multiple Modes of Information Using Unsupervised Neural Classifiers.- Neural Net Ensembles for Lithology Recognition.- Improving Performance of a Multiple Classifier System Using Self-generating Neural Networks.- Ensemble Strategies.- Negative Correlation Learning and the Ambiguity Family of Ensemble Methods.- Spectral Coefficients and Classifier Correlation.- Ensemble Construction via Designed Output Distortion.- Simulating Classifier Outputs for Evaluating Parallel Combination Methods.- A New Ensemble Diversity Measure Applied to Thinning Ensembles.- Ensemble Methods for Noise Elimination in Classification Problems.- Applications.- New Boosting Algorithms for Classification Problems with Large Number of Classes Applied to a Handwritten Word Recognition Task.- Automatic Target Recognition Using Multiple Description Coding Models for Multiple Classifier Systems.- A Modular Multiple Classifier System for the Detection of Intrusions in Computer Networks.- Input Space Transformations for Multi-classifier Systems Based on n-tuple Classifiers with Application to Handwriting Recognition.- Building Classifier Ensembles for Automatic Sports Classification.- Classification of Aircraft Maneuvers for Fault Detection.- Solving Problems Two at a Time: Classification of Web Pages Using a Generic Pair-Wise Multiple Classifier System.- Design and Evaluation of an Adaptive Combination Framework for OCR Result Strings.