Multiple Classifier Systems: 4th International Workshop, McS 2003, Guildford, Uk, June 11-13, 2003 : Proceedings

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Format: Paperback
Pub. Date: 2003-08-01
Publisher(s): Springer Verlag
List Price: $127.32

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Summary

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

Table of Contents

Data Dependence in Combining Classifiersp. 1
Boosting with Averaged Weight Vectorsp. 15
Error Bounds for Aggressive and Conservative AdaBoostp. 25
An Empirical Comparison of Three Boosting Algorithms on Real Data Sets with Artificial Class Noisep. 35
The Beneficial Effects of Using Multi-net Systems That Focus on Hard Patternsp. 45
The Behavior Knowledge Space Fusion Method: Analysis of Generalization Error and Strategies for Performance Improvementp. 55
Reducing the Overconfidence of Base Classifiers when Combining Their Decisionsp. 65
Linear Combiners for Classifier Fusion: Some Theoretical and Experimental Resultsp. 74
Comparison of Classifier Selection Methods for Improving Committee Performancep. 84
Towards Automated Classifier Combination for Pattern Recognitionp. 94
Serial Multiple Classifier Systems Exploiting a Coarse to Fine Output Codingp. 106
Polychotomous Classification with Pairwise Classifiers: A New Voting Principlep. 115
Multi-category Classification by Soft-Max Combination of Binary Classifiersp. 125
A Sequential Scheduling Approach to Combining Multiple Object Classifiers Using Cross-Entropyp. 135
Binary Classifier Fusion Based on the Basic Decomposition Methodsp. 146
Good Error Correcting Output Codes for Adaptive Multiclass Learningp. 156
Finding Natural Clusters Using Multi-clusterer Combiner Based on Shared Nearest Neighborsp. 166
An Ensemble Approach for Data Fusion with Learn++p. 176
The Practical Performance Characteristics of Tomographically Filtered Multiple Classifier Fusionp. 186
Accumulated-Recognition-Rate Normalization for Combining Multiple On/Off-Line Japanese Character Classifiers Tested on a Large Databasep. 196
Beam Search Extraction and Forgetting Strategies on Shared Ensemblesp. 206
A Markov Chain Approach to Multiple Classifier Fusionp. 217
A Study of Ensemble of Hybrid Networks with Strong Regularizationp. 227
Combining Multiple Modes of Information Using Unsupervised Neural Classifiersp. 236
Neural Net Ensembles for Lithology Recognitionp. 246
Improving Performance of a Multiple Classifier System Using Self-generating Neural Networksp. 256
Negative Correlation Learning and the Ambiguity Family of Ensemble Methodsp. 266
Spectral Coefficients and Classifier Correlationp. 276
Ensemble Construction via Designed Output Distortionp. 286
Simulating Classifier Outputs for Evaluating Parallel Combination Methodsp. 296
A New Ensemble Diversity Measure Applied to Thinning Ensemblesp. 306
Ensemble Methods for Noise Elimination in Classification Problemsp. 317
New Boosting Algorithms for Classification Problems with Large Number of Classes Applied to a Handwritten Word Recognition Taskp. 326
Automatic Target Recognition Using Multiple Description Coding Models for Multiple Classifier Systemsp. 336
A Modular Multiple Classifier System for the Detection of Intrusions in Computer Networksp. 346
Input Space Transformations for Multi-classifier Systems Based on n-tuple Classifiers with Application to Handwriting Recognitionp. 356
Building Classifier Ensembles for Automatic Sports Classificationp. 366
Classification of Aircraft Maneuvers for Fault Detectionp. 375
Solving Problems Two at a Time: Classification of Web Pages Using a Generic Pair-Wise Multiple Classifier Systemp. 385
Design and Evaluation of an Adaptive Combination Framework for OCR Result Stringsp. 395
Author Indexp. 405
Table of Contents provided by Blackwell. All Rights Reserved.

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