Invited Paper

• 
  Mohamed S. Kamel, Nayer M. Wanas: Data Dependence in Combining Classifiers. 1-14

Boosting

• 
  Nikunj C. Oza: Boosting with Averaged Weight Vectors. 15-24
• 
  Ludmila I. Kuncheva: Error Bounds for Aggressive and Conservative AdaBoost. 25-34
• 
  Ross A. McDonald, David J. Hand, Idris A. Eckley: An Empirical Comparison of Three Boosting Algorithms on Real Data Sets with Artificial Class Noise. 35-44
• 
  Jerónimo Arenas-García, Aníbal R. Figueiras-Vidal, Amanda J. C. Sharkey: The Beneficial Effects of Using Multi-net Systems That Focus on Hard Patterns. 45-54

Combination Rules

• 
  Sarunas Raudys, Fabio Roli: The Behavior Knowledge Space Fusion Method: Analysis of Generalization Error and Strategies for Performance Improvement. 55-64
• 
  Sarunas Raudys, Ray L. Somorjai, Richard Baumgartner: Reducing the Overconfidence of Base Classifiers when Combining Their Decisions. 65-73
• 
  Giorgio Fumera, Fabio Roli: Linear Combiners for Classifier Fusion: Some Theoretical and Experimental Results. 74-83
• 
  Matti Aksela: Comparison of Classifier Selection Methods for Improving Committee Performance. 84-93
• 
  Alper Baykut, Aytül Erçil: Towards Automated Classifier Combination for Pattern Recognition. 94-105

Multi-class Methods

• 
  Josef Kittler, Alireza Ahmadyfard, David Windridge: Serial Multiple Classifier Systems Exploiting a Coarse to Fine Output Coding. 106-114
• 
  Florin Cutzu: Polychotomous Classification with Pairwise Classifiers: A New Voting Principle. 115-124
• 
  Kaibo Duan, S. Sathiya Keerthi, Wei Chu, Shirish Krishnaj Shevade, Aun Neow Poo: Multi-category Classification by Soft-Max Combination of Binary Classifiers. 125-134
• 
  Derek R. Magee: A Sequential Scheduling Approach to Combining Multiple Object Classifiers Using Cross-Entropy. 135-145
• 
  Jaepil Ko, Hyeran Byun: Binary Classifier Fusion Based on the Basic Decomposition Methods. 146-155
• 
  Elizabeth Tapia, José Carlos González, L. Javier García-Villalba: Good Error Correcting Output Codes for Adaptive Multiclass Learning. 156-165

Fusion Schemes Architectures

• 
  Hanan Ayad, Mohamed S. Kamel: Finding Natural Clusters Using Multi-clusterer Combiner Based on Shared Nearest Neighbors. 166-175
• 
  Michael Lewitt, Robi Polikar: An Ensemble Approach for Data Fusion with Learn++. 176-185
• 
  David Windridge, Josef Kittler: The Practical Performance Characteristics of Tomographically Filtered Multiple Classifier Fusion. 186-195
• 
  Ondrej Velek, Stefan Jäger, Masaki Nakagawa: Accumulated-Recognition-Rate Normalization for Combining Multiple On/Off-Line Japanese Character Classifiers Tested on a Large Database. 196-205
• 
  Vicent Estruch, César Ferri, José Hernández-Orallo, M. José Ramírez-Quintana: Beam Search Extraction and Forgetting Strategies on Shared Ensembles. 206-216
• 
  Stephen P. Luttrell: A Markov Chain Approach to Multiple Classifier Fusion. 217-226

Neural Network Ensembles

• 
  Shimon Cohen, Nathan Intrator: A Study of Ensemble of Hybrid Networks with Strong Regularization. 227-235
• 
  Khurshid Ahmad, Matthew C. Casey, Bogdan Vrusias, Panagiotis Saragiotis: Combining Multiple Modes of Information Using Unsupervised Neural Classifiers. 236-245
• 
  Rafael Valle dos Santos, Marley B. R. Vellasco, Fredy Artola, Sérgio da Fontoura: Neural Net Ensembles for Lithology Recognition. 246-255
• 
  Hirotaka Inoue, Hiroyuki Narihisa: Improving Performance of a Multiple Classifier System Using Self-generating Neural Networks. 256-265

Ensemble Strategies

• 
  Gavin Brown, Jeremy L. Wyatt: Negative Correlation Learning and the Ambiguity Family of Ensemble Methods. 266-275
• 
  Terry Windeatt, Reza Ghaderi, Gholamreza Ardeshir: Spectral Coefficients and Classifier Correlation. 276-285
• 
  Stefan W. Christensen: Ensemble Construction via Designed Output Distortion. 286-295
• 
  Héla Zouari, Laurent Heutte, Yves Lecourtier, Adel M. Alimi: Simulating Classifier Outputs for Evaluating Parallel Combination Methods. 296-305
• 
  Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer: A New Ensemble Diversity Measure Applied to Thinning Ensembles. 306-316
• 
  Sofie Verbaeten, Anneleen Van Assche: Ensemble Methods for Noise Elimination in Classification Problems. 317-325

Applications

• 
  Simon Günter, Horst Bunke: New Boosting Algorithms for Classification Problems with Large Number of Classes Applied to a Handwritten Word Recognition Task. 326-335
• 
  Widhyakorn Asdornwised, Somchai Jitapunkul: Automatic Target Recognition Using Multiple Description Coding Models for Multiple Classifier Systems. 336-345
• 
  Giorgio Giacinto, Fabio Roli, Luca Didaci: A Modular Multiple Classifier System for the Detection of Intrusions in Computer Networks. 346-355
• 
  Konstantinos Sirlantzis, Sanaul Hoque, Michael C. Fairhurst: Input Space Transformations for Multi-classifier Systems Based on n-tuple Classifiers with Application to Handwriting Recognition. 356-365
• 
  Edward Jaser, Josef Kittler, William J. Christmas: Building Classifier Ensembles for Automatic Sports Classification. 366-374
• 
  Nikunj C. Oza, Kagan Tumer, Irem Y. Tumer, Edward M. Huff: Classification of Aircraft Maneuvers for Fault Detection. 375-384
• 
  Hassan Alam, Ahmad Fuad Rezaur Rahman, Yuliya Tarnikova: Solving Problems Two at a Time: Classification of Web Pages Using a Generic Pair-Wise Multiple Classifier System. 385-394
• 
  Elke Wilczok, Wolfgang Lellmann: Design and Evaluation of an Adaptive Combination Framework for OCR Result Strings. 395-404

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