Table of Contents

• Data Dependence in Combining ClassifiersMohamed S. Kamel, Nayer M. Wanas. 1-14 [doi]
• Boosting with Averaged Weight VectorsNikunj C. Oza. 15-24 [doi]
• Error Bounds for Aggressive and Conservative AdaBoostLudmila I. Kuncheva. 25-34 [doi]
• An Empirical Comparison of Three Boosting Algorithms on Real Data Sets with Artificial Class NoiseRoss A. McDonald, David J. Hand, Idris A. Eckley. 35-44 [doi]
• The Beneficial Effects of Using Multi-net Systems That Focus on Hard PatternsJerónimo Arenas-García, Aníbal R. Figueiras-Vidal, Amanda J. C. Sharkey. 45-54 [doi]
• The Behavior Knowledge Space Fusion Method: Analysis of Generalization Error and Strategies for Performance ImprovementSarunas Raudys, Fabio Roli. 55-64 [doi]
• Reducing the Overconfidence of Base Classifiers when Combining Their DecisionsSarunas Raudys, Ray L. Somorjai, Richard Baumgartner. 65-73 [doi]
• Linear Combiners for Classifier Fusion: Some Theoretical and Experimental ResultsGiorgio Fumera, Fabio Roli. 74-83 [doi]
• Comparison of Classifier Selection Methods for Improving Committee PerformanceMatti Aksela. 84-93 [doi]
• Towards Automated Classifier Combination for Pattern RecognitionAlper Baykut, Aytül Erçil. 94-105 [doi]
• Serial Multiple Classifier Systems Exploiting a Coarse to Fine Output CodingJosef Kittler, Alireza Ahmadyfard, David Windridge. 106-114 [doi]
• Polychotomous Classification with Pairwise Classifiers: A New Voting PrincipleFlorin Cutzu. 115-124 [doi]
• Multi-category Classification by Soft-Max Combination of Binary ClassifiersKaibo Duan, S. Sathiya Keerthi, Wei Chu, Shirish Krishnaj Shevade, Aun Neow Poo. 125-134 [doi]
• A Sequential Scheduling Approach to Combining Multiple Object Classifiers Using Cross-EntropyDerek R. Magee. 135-145 [doi]
• Binary Classifier Fusion Based on the Basic Decomposition MethodsJaepil Ko, Hyeran Byun. 146-155 [doi]
• Good Error Correcting Output Codes for Adaptive Multiclass LearningElizabeth Tapia, José Carlos González, L. Javier García-Villalba. 156-165 [doi]
• Finding Natural Clusters Using Multi-clusterer Combiner Based on Shared Nearest NeighborsHanan Ayad, Mohamed S. Kamel. 166-175 [doi]
• An Ensemble Approach for Data Fusion with Learn++Michael Lewitt, Robi Polikar. 176-185 [doi]
• The Practical Performance Characteristics of Tomographically Filtered Multiple Classifier FusionDavid Windridge, Josef Kittler. 186-195 [doi]
• Accumulated-Recognition-Rate Normalization for Combining Multiple On/Off-Line Japanese Character Classifiers Tested on a Large DatabaseOndrej Velek, Stefan Jäger, Masaki Nakagawa. 196-205 [doi]
• Beam Search Extraction and Forgetting Strategies on Shared EnsemblesVicent Estruch, César Ferri, José Hernández-Orallo, M. José Ramírez-Quintana. 206-216 [doi]
• A Markov Chain Approach to Multiple Classifier FusionStephen P. Luttrell. 217-226 [doi]
• A Study of Ensemble of Hybrid Networks with Strong RegularizationShimon Cohen, Nathan Intrator. 227-235 [doi]
• Combining Multiple Modes of Information Using Unsupervised Neural ClassifiersKhurshid Ahmad, Matthew C. Casey, Bogdan Vrusias, Panagiotis Saragiotis. 236-245 [doi]
• Neural Net Ensembles for Lithology RecognitionRafael Valle dos Santos, Marley B. R. Vellasco, Fredy Artola, Sérgio da Fontoura. 246-255 [doi]
• Improving Performance of a Multiple Classifier System Using Self-generating Neural NetworksHirotaka Inoue, Hiroyuki Narihisa. 256-265 [doi]
• Negative Correlation Learning and the Ambiguity Family of Ensemble MethodsGavin Brown, Jeremy L. Wyatt. 266-275 [doi]
• Spectral Coefficients and Classifier CorrelationTerry Windeatt, Reza Ghaderi, Gholamreza Ardeshir. 276-285 [doi]
• Ensemble Construction via Designed Output DistortionStefan W. Christensen. 286-295 [doi]
• Simulating Classifier Outputs for Evaluating Parallel Combination MethodsHéla Zouari, Laurent Heutte, Yves Lecourtier, Adel M. Alimi. 296-305 [doi]
• A New Ensemble Diversity Measure Applied to Thinning EnsemblesRobert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer. 306-316 [doi]
• Ensemble Methods for Noise Elimination in Classification ProblemsSofie Verbaeten, Anneleen Van Assche. 317-325 [doi]
• New Boosting Algorithms for Classification Problems with Large Number of Classes Applied to a Handwritten Word Recognition TaskSimon Günter, Horst Bunke. 326-335 [doi]
• Automatic Target Recognition Using Multiple Description Coding Models for Multiple Classifier SystemsWidhyakorn Asdornwised, Somchai Jitapunkul. 336-345 [doi]
• A Modular Multiple Classifier System for the Detection of Intrusions in Computer NetworksGiorgio Giacinto, Fabio Roli, Luca Didaci. 346-355 [doi]
• Input Space Transformations for Multi-classifier Systems Based on n-tuple Classifiers with Application to Handwriting RecognitionKonstantinos Sirlantzis, Sanaul Hoque, Michael C. Fairhurst. 356-365 [doi]
• Building Classifier Ensembles for Automatic Sports ClassificationEdward Jaser, Josef Kittler, William J. Christmas. 366-374 [doi]
• Classification of Aircraft Maneuvers for Fault DetectionNikunj C. Oza, Kagan Tumer, Irem Y. Tumer, Edward M. Huff. 375-384 [doi]
• Solving Problems Two at a Time: Classification of Web Pages Using a Generic Pair-Wise Multiple Classifier SystemHassan Alam, Ahmad Fuad Rezaur Rahman, Yuliya Tarnikova. 385-394 [doi]
• Design and Evaluation of an Adaptive Combination Framework for OCR Result StringsElke Wilczok, Wolfgang Lellmann. 395-404 [doi]