Multiple Classifier Systems: 4th International Workshop, McS 2003, Guildford, Uk, June 11-13, 2003 : Proceedings
by Windeatt, Terry; Windeatt, Terry; Roli, Fabio; Roli, FabioRent Textbook
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Summary
Table of Contents
| Data Dependence in Combining Classifiers | p. 1 |
| Boosting with Averaged Weight Vectors | p. 15 |
| Error Bounds for Aggressive and Conservative AdaBoost | p. 25 |
| An Empirical Comparison of Three Boosting Algorithms on Real Data Sets with Artificial Class Noise | p. 35 |
| The Beneficial Effects of Using Multi-net Systems That Focus on Hard Patterns | p. 45 |
| The Behavior Knowledge Space Fusion Method: Analysis of Generalization Error and Strategies for Performance Improvement | p. 55 |
| Reducing the Overconfidence of Base Classifiers when Combining Their Decisions | p. 65 |
| Linear Combiners for Classifier Fusion: Some Theoretical and Experimental Results | p. 74 |
| Comparison of Classifier Selection Methods for Improving Committee Performance | p. 84 |
| Towards Automated Classifier Combination for Pattern Recognition | p. 94 |
| Serial Multiple Classifier Systems Exploiting a Coarse to Fine Output Coding | p. 106 |
| Polychotomous Classification with Pairwise Classifiers: A New Voting Principle | p. 115 |
| Multi-category Classification by Soft-Max Combination of Binary Classifiers | p. 125 |
| A Sequential Scheduling Approach to Combining Multiple Object Classifiers Using Cross-Entropy | p. 135 |
| Binary Classifier Fusion Based on the Basic Decomposition Methods | p. 146 |
| Good Error Correcting Output Codes for Adaptive Multiclass Learning | p. 156 |
| Finding Natural Clusters Using Multi-clusterer Combiner Based on Shared Nearest Neighbors | p. 166 |
| An Ensemble Approach for Data Fusion with Learn++ | p. 176 |
| The Practical Performance Characteristics of Tomographically Filtered Multiple Classifier Fusion | p. 186 |
| Accumulated-Recognition-Rate Normalization for Combining Multiple On/Off-Line Japanese Character Classifiers Tested on a Large Database | p. 196 |
| Beam Search Extraction and Forgetting Strategies on Shared Ensembles | p. 206 |
| A Markov Chain Approach to Multiple Classifier Fusion | p. 217 |
| A Study of Ensemble of Hybrid Networks with Strong Regularization | p. 227 |
| Combining Multiple Modes of Information Using Unsupervised Neural Classifiers | p. 236 |
| Neural Net Ensembles for Lithology Recognition | p. 246 |
| Improving Performance of a Multiple Classifier System Using Self-generating Neural Networks | p. 256 |
| Negative Correlation Learning and the Ambiguity Family of Ensemble Methods | p. 266 |
| Spectral Coefficients and Classifier Correlation | p. 276 |
| Ensemble Construction via Designed Output Distortion | p. 286 |
| Simulating Classifier Outputs for Evaluating Parallel Combination Methods | p. 296 |
| A New Ensemble Diversity Measure Applied to Thinning Ensembles | p. 306 |
| Ensemble Methods for Noise Elimination in Classification Problems | p. 317 |
| New Boosting Algorithms for Classification Problems with Large Number of Classes Applied to a Handwritten Word Recognition Task | p. 326 |
| Automatic Target Recognition Using Multiple Description Coding Models for Multiple Classifier Systems | p. 336 |
| A Modular Multiple Classifier System for the Detection of Intrusions in Computer Networks | p. 346 |
| Input Space Transformations for Multi-classifier Systems Based on n-tuple Classifiers with Application to Handwriting Recognition | p. 356 |
| Building Classifier Ensembles for Automatic Sports Classification | p. 366 |
| Classification of Aircraft Maneuvers for Fault Detection | p. 375 |
| Solving Problems Two at a Time: Classification of Web Pages Using a Generic Pair-Wise Multiple Classifier System | p. 385 |
| Design and Evaluation of an Adaptive Combination Framework for OCR Result Strings | p. 395 |
| Author Index | p. 405 |
| Table of Contents provided by Blackwell. All Rights Reserved. |
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