This book constitutes the refereed proceedings of the 4th International Workshop on Multiple Classifier Systems, MCS 2003, held in Guildford, UK in June 2003.
The 40 revised full papers presented with one invited paper were carefully reviewed and selected for presentation. The papers are organized in topical sections on boosting, combination rules, multi-class methods, fusion schemes and architectures, neural network ensembles, ensemble strategies, and applications
Author(s): Mohamed S. Kamel, Nayer M. Wanas (auth.), Terry Windeatt, Fabio Roli (eds.)
Series: Lecture Notes in Computer Science 2709
Edition: 1
Publisher: Springer-Verlag Berlin Heidelberg
Year: 2003
Language: English
Pages: 414
Tags: Pattern Recognition; Computation by Abstract Devices; Artificial Intelligence (incl. Robotics); Image Processing and Computer Vision
Data Dependence in Combining Classifiers....Pages 1-14
Boosting with Averaged Weight Vectors....Pages 15-24
Error Bounds for Aggressive and Conservative AdaBoost....Pages 25-34
An Empirical Comparison of Three Boosting Algorithms on Real Data Sets with Artificial Class Noise....Pages 35-44
The Beneficial Effects of Using Multi-net Systems That Focus on Hard Patterns....Pages 45-54
The Behavior Knowledge Space Fusion Method: Analysis of Generalization Error and Strategies for Performance Improvement....Pages 55-64
Reducing the Overconfidence of Base Classifiers when Combining Their Decisions....Pages 65-73
Linear Combiners for Classifier Fusion: Some Theoretical and Experimental Results....Pages 74-83
Comparison of Classifier Selection Methods for Improving Committee Performance....Pages 84-93
Towards Automated Classifier Combination for Pattern Recognition....Pages 94-105
Serial Multiple Classifier Systems Exploiting a Coarse to Fine Output Coding....Pages 106-114
Polychotomous Classification with Pairwise Classifiers: A New Voting Principle....Pages 115-124
Multi-category Classification by Soft-Max Combination of Binary Classifiers....Pages 125-134
A Sequential Scheduling Approach to Combining Multiple Object Classifiers Using Cross-Entropy....Pages 135-145
Binary Classifier Fusion Based on the Basic Decomposition Methods....Pages 146-155
Good Error Correcting Output Codes for Adaptive Multiclass Learning....Pages 156-165
Finding Natural Clusters Using Multi-clusterer Combiner Based on Shared Nearest Neighbors....Pages 166-175
An Ensemble Approach for Data Fusion with Learn++....Pages 176-185
The Practical Performance Characteristics of Tomographically Filtered Multiple Classifier Fusion....Pages 186-195
Accumulated-Recognition-Rate Normalization for Combining Multiple On/Off-Line Japanese Character Classifiers Tested on a Large Database....Pages 196-205
Beam Search Extraction and Forgetting Strategies on Shared Ensembles....Pages 206-216
A Markov Chain Approach to Multiple Classifier Fusion....Pages 217-226
A Study of Ensemble of Hybrid Networks with Strong Regularization....Pages 227-235
Combining Multiple Modes of Information Using Unsupervised Neural Classifiers....Pages 236-245
Neural Net Ensembles for Lithology Recognition....Pages 246-255
Improving Performance of a Multiple Classifier System Using Self-generating Neural Networks....Pages 256-265
Negative Correlation Learning and the Ambiguity Family of Ensemble Methods....Pages 266-275
Spectral Coefficients and Classifier Correlation....Pages 276-285
Ensemble Construction via Designed Output Distortion....Pages 286-295
Simulating Classifier Outputs for Evaluating Parallel Combination Methods....Pages 296-305
A New Ensemble Diversity Measure Applied to Thinning Ensembles....Pages 306-316
Ensemble Methods for Noise Elimination in Classification Problems....Pages 317-325
New Boosting Algorithms for Classification Problems with Large Number of Classes Applied to a Handwritten Word Recognition Task....Pages 326-335
Automatic Target Recognition Using Multiple Description Coding Models for Multiple Classifier Systems....Pages 336-345
A Modular Multiple Classifier System for the Detection of Intrusions in Computer Networks....Pages 346-355
Input Space Transformations for Multi-classifier Systems Based on n-tuple Classifiers with Application to Handwriting Recognition....Pages 356-365
Building Classifier Ensembles for Automatic Sports Classification....Pages 366-374
Classification of Aircraft Maneuvers for Fault Detection....Pages 375-384
Solving Problems Two at a Time: Classification of Web Pages Using a Generic Pair-Wise Multiple Classifier System....Pages 385-394
Design and Evaluation of an Adaptive Combination Framework for OCR Result Strings....Pages 395-404
