Table of Contents

• Bagging and the Random Subspace Method for Redundant Feature SpacesMarina Skurichina, Robert P. W. Duin. 1-10 [doi]
• Performance Degradation in BoostingJeevani Wickramaratna, Sean B. Holden, Bernard F. Buxton. 11-21 [doi]
• A Generalized Class of Boosting Algorithms Based on Recursive Decoding ModelsElizabeth Tapia, José Carlos González, Julio Villena. 22-31 [doi]
• Tuning Cost-Sensitive Boosting and Its Application to Melanoma DiagnosisStefano Merler, Cesare Furlanello, Barbara Larcher, Andrea Sboner. 32-42 [doi]
• Learning Classification RBF Networks by BoostingJuan J. Rodríguez Diez, Carlos Alonso González. 43-52 [doi]
• Data Complexity Analysis for Classifier CombinationTin Kam Ho. 53-67 [doi]
• Genetic Programming for Improved Receiver Operating CharacteristicsWilliam B. Langdon, Bernard F. Buxton. 68-77 [doi]
• Methods for Designing Multiple Classifier SystemsFabio Roli, Giorgio Giacinto, Gianni Vernazza. 78-87 [doi]
• Decision-Level Fusion in Fingerprint VerificationSalil Prabhakar, Anil K. Jain. 88-98 [doi]
• Genetic Algorithms for Multi-classifier System Configuration: A Case Study in Character RecognitionKonstantinos Sirlantzis, Michael C. Fairhurst, Sanaul Hoque. 99-108 [doi]
• Combined Classification of Handwritten Digits Using the Virtual Test Sample Method Jörg Dahmen, Daniel Keysers, Hermann Ney. 109-118 [doi]
• Averaging Weak ClassifiersDechang Chen, Jian Liu. 119-125 [doi]
• Mixing a Symbolic and a Subsymbolic Expert to Improve Carcinogenicity Prediction of Aromatic CompoundsGiuseppina C. Gini, Marco Lorenzini, Emilio Benfenati, Raffaella Brambilla, Luca Malvé. 126-135 [doi]
• Multiple Classifier Systems Based on Interpretable Linear ClassifiersDavid J. Hand, Niall M. Adams, Mark G. Kelly. 136-147 [doi]
• Least Squares and Estimation Measures via Error Correcting Output CodeReza Ghaderi, Terry Windeatt. 148-157 [doi]
• Dependence among Codeword Bits Errors in ECOC Learning Machines: An Experimental AnalysisFrancesco Masulli, Giorgio Valentini. 158-167 [doi]
• Information Analysis of Multiple Classifier FusionJiri Grim, Josef Kittler, Pavel Pudil, Petr Somol. 168-177 [doi]
• Limiting the Number of Trees in Random ForestsPatrice Latinne, Olivier Debeir, Christine Decaestecker. 178-187 [doi]
• Learning-Data Selection Mechanism through Neural Networks EnsemblePitoyo Hartono, Shuji Hashimoto. 188-197 [doi]
• A Multi-SVM Classification SystemDimitrios S. Frossyniotis, Andreas Stafylopatis. 198-207 [doi]
• Automatic Classification of Clustered Microcalcifications by a Multiple Classifier SystemPasquale Foggia, Carlo Sansone, Francesco Tortorella, Mario Vento. 208-217 [doi]
• Feature Weighted Ensemble Classifiers - A Modified Decision SchemeThomas Martini Jørgensen, Christian Linneberg. 218-227 [doi]
• Feature Subsets for Classifier Combination: An Enumerative ExperimentLudmila I. Kuncheva, Christopher J. Whitaker. 228-237 [doi]
• Input Decimation Ensembles: Decorrelation through Dimensionality ReductionNikunj C. Oza, Kagan Tumer. 238-247 [doi]
• Classifier Combination as a Tomographic ProcessDavid Windridge, Josef Kittler. 248-258 [doi]
• A Robust Multiple Classifier System for a Partially Unsupervised Updating of Land-Cover MapsLorenzo Bruzzone, Roberto Cossu. 259-268 [doi]
• Combining Supervised Remote Sensing Image Classifiers Based on Individual Class PerformancesPaul C. Smits. 269-278 [doi]
• Boosting, Bagging, and Consensus Based Classification of Multisource Remote Sensing DataGunnar Jakob Briem, Jon Atli Benediktsson, Johannes R. Sveinsson. 279-288 [doi]
• Solar Wind Data Analysis Using Self-Organizing Hierarchical Neural Network ClassifiersS. A. Dolenko, Yu. V. Orlov, I. G. Persiantsev, Ju. S. Shugai, A. V. Dmitriev, A. V. Suvorova, I. S. Veselovsky. 289-298 [doi]
• Combining One-Class ClassifiersDavid M. J. Tax, Robert P. W. Duin. 299-308 [doi]
• Finding Consistent Clusters in Data PartitionsAna L. N. Fred. 309-318 [doi]
• A Self-Organising Approach to Multiple Classifier FusionStephen P. Luttrell. 319-328 [doi]
• Error Rejection in Linearly Combined Multiple ClassifiersGiorgio Fumera, Fabio Roli. 329-338 [doi]
• Relationship of Sum and Vote Fusion StrategiesJosef Kittler, Fuad M. Alkoot. 339-348 [doi]
• Complexity of Data Subsets Generated by the Random Subspace Method: An Experimental InvestigationLudmila I. Kuncheva, Fabio Roli, Gian Luca Marcialis, Catherine A. Shipp. 349-358 [doi]
• On Combining Dissimilarity RepresentationsElzbieta Pekalska, Robert P. W. Duin. 359-368 [doi]
• Application of Multiple Classifier Techniques to Subband Speaker Identification with an HMM/ANN SystemJonathan E. Higgins, Tony J. Dodd, Robert I. Damper. 369-377 [doi]
• Classification of Time Series Utilizing Temporal and Decision FusionChristian Dietrich, Friedhelm Schwenker, Günther Palm. 378-387 [doi]
• Use of Positional Information in Sequence Alignment for Multiple Classifier CombinationUrs-Viktor Marti, Horst Bunke. 388-398 [doi]
• Application of the Evolutionary Algorithms for Classifier Selection in Multiple Classifier Systems with Majority VotingDymitr Ruta, Bogdan Gabrys. 399-408 [doi]
• Tree-Structured Support Vector Machines for Multi-class Pattern RecognitionFriedhelm Schwenker, Günther Palm. 409-417 [doi]
• On the Combination of Different Template Matching Strategies for Fast Face DetectionBernhard Fröba, Walter Zink. 418-428 [doi]
• Improving Product by Moderating k-NN ClassifiersFuad M. Alkoot, Josef Kittler. 429-439 [doi]
• Automatic Model Selection in a Hybrid Perceptron/Radial NetworkShimon Cohen, Nathan Intrator. 440-454 [doi]