Table of Contents

• The Bias Variance Trade-Off in Bootstrapped Error Correcting Output Code EnsemblesRaymond S. Smith, Terry Windeatt. 1-10 [doi]
• Recoding Error-Correcting Output CodesSergio Escalera, Oriol Pujol, Petia Radeva. 11-21 [doi]
• Comparison of Bagging and Boosting Algorithms on Sample and Feature WeightingSatoshi Shirai, Mineichi Kudo, Atsuyoshi Nakamura. 22-31 [doi]
• Multi-class Boosting with Class HierarchiesGoo Jun, Joydeep Ghosh. 32-41 [doi]
• Hybrid Hierarchical Classifiers for Hyperspectral Data AnalysisGoo Jun, Joydeep Ghosh. 42-51 [doi]
• Multiple Classifier Combination for Hyperspectral Remote Sensing Image ClassificationPeijun Du, Wei Zhang, Hao Sun. 52-61 [doi]
• Ensemble Strategies for Classifying Hyperspectral Remote Sensing DataXavier Ceamanos, Björn Waske, Jon Atli Benediktsson, Jocelyn Chanussot, Johannes R. Sveinsson. 62-71 [doi]
• Optimal Mean-Precision ClassifierDavid M. J. Tax, Marco Loog, Robert P. W. Duin. 72-81 [doi]
• A Multiple Expert Approach to the Class Imbalance Problem Using Inverse Random under SamplingMuhammad Atif Tahir, Josef Kittler, Krystian Mikolajczyk, Fei Yan. 82-91 [doi]
• Decision Templates Based RBF Network for Tree-Structured Multiple Classifier FusionMohamed Farouk Abdel Hady, Friedhelm Schwenker. 92-101 [doi]
• Efficient Online Classification Using an Ensemble of Bayesian Linear Logistic RegressorsNarayanan U. Edakunni, Sethu Vijayakumar. 102-111 [doi]
• Regularized Linear Models in Stacked GeneralizationSamuel Robert Reid, Gregory Z. Grudic. 112-121 [doi]
• Active Grading Ensembles for Learning Visual Quality Control from Multiple HumansDavy Sannen, Hendrik Van Brussel. 122-131 [doi]
• Multiple Classifier Systems for Adversarial Classification TasksBattista Biggio, Giorgio Fumera, Fabio Roli. 132-141 [doi]
• Incremental Learning of Variable Rate Concept DriftRyan Elwell, Robi Polikar. 142-151 [doi]
• Semi-supervised Co-update of Multiple MatchersLuca Didaci, Gian Luca Marcialis, Fabio Roli. 152-160 [doi]
• Handling Multimodal Information Fusion with Missing Observations Using the Neutral Point Substitution MethodDavid Windridge, Norman Poh, Vadim Mottl, Alexander Tatarchuk, Andrey Eliseyev. 161-170 [doi]
• Influence of Hyperparameters on Random Forest AccuracySimon Bernard, Laurent Heutte, Sébastien Adam. 171-180 [doi]
• Ensembles of One Class Support Vector MachinesAlbert D. Shieh, David F. Kamm. 181-190 [doi]
• Disturbing Neighbors Ensembles for Linear SVMJesús Maudes, Juan José Rodríguez, Cesar García-Osorio. 191-200 [doi]
• A Labelled Graph Based Multiple Classifier SystemWan-Jui Lee, Robert P. W. Duin. 201-210 [doi]
• Cluster Ensembles Based on Vector Space Embeddings of GraphsKaspar Riesen, Horst Bunke. 211-221 [doi]
• Random Ordinality Ensembles A Novel Ensemble Method for Multi-valued Categorical DataAmir Ahmad, Gavin Brown. 222-231 [doi]
• True Path Rule Hierarchical EnsemblesGiorgio Valentini. 232-241 [doi]
• A Study of Semi-supervised Generative EnsemblesManuela Zanda, Gavin Brown. 242-251 [doi]
• Hierarchical Ensemble Support Cluster MachineMingmin Chi, Youdong Miao, Youze Tang, Jon Atli Benediktsson, Xuanjing Huang. 252-261 [doi]
• Multi-scale Stacked Sequential LearningOriol Pujol, Eloi Puertas, Carlo Gatta. 262-271 [doi]
• Unsupervised Hierarchical Weighted Multi-segmenterMichal Haindl, Stanislav Mikes, Pavel Pudil. 272-282 [doi]
• Ant Clustering Using Ensembles of PartitionsYuhua Gu, Lawrence O. Hall, Dmitry B. Goldgof. 283-292 [doi]
• Selective Ensemble under Regularization FrameworkNan Li, Zhi-Hua Zhou. 293-303 [doi]
• Criteria Ensembles in Feature SelectionPetr Somol, Jiri Grim, Pavel Pudil. 304-313 [doi]
• Network Protocol Verification by a Classifier Selection EnsembleFrancesco Gargiulo, Ludmila I. Kuncheva, Carlo Sansone. 314-323 [doi]
• Supervised Selective Combining Pattern Recognition Modalities and Its Application to Signature Verification by Fusing On-Line and Off-Line KernelsAlexander Tatarchuk, Valentina Sulimova, David Windridge, Vadim Mottl, Mikhail Lange. 324-334 [doi]
• Improved Uniformity Enforcement in Stochastic DiscriminationMatthew Prior, Terry Windeatt. 335-343 [doi]
• An Information Theoretic Perspective on Multiple Classifier SystemsGavin Brown. 344-353 [doi]
• Constraints in Weighted AveragingAmber Tomas. 354-363 [doi]
• FaSS: Ensembles for Stable LearnersKai Ming Ting, Jonathan R. Wells, Swee Chuan Tan, Shyh Wei Teng, Geoffrey I. Webb. 364-374 [doi]
• Classifying Remote Sensing Data with Support Vector Machines and Imbalanced Training DataBjörn Waske, Jon Atli Benediktsson, Johannes R. Sveinsson. 375-384 [doi]
• Terrain Segmentation with On-Line Mixtures of Experts for Autonomous Robot NavigationMichael J. Procopio, W. Philip Kegelmeyer, Gregory Z. Grudic, Jane Mulligan. 385-397 [doi]
• Consistency Measure of Multiple Classifiers for Land Cover Classification by Remote Sensing ImagePeijun Du, Guangli Li, Wei Zhang, Xiaomei Wang, Hao Sun. 398-407 [doi]
• Target Identification from High Resolution Remote Sensing Image by Combining Multiple ClassifiersPeijun Du, Hao Sun, Wei Zhang. 408-417 [doi]
• Neural Network Optimization for Combinations in Identification SystemsSergey Tulyakov, Venu Govindaraju. 418-427 [doi]
• MLP, Gaussian Processes and Negative Correlation Learning for Time Series PredictionWaleed M. Azmy, Neamat El Gayar, Amir F. Atiya, Hisham El-Shishiny. 428-437 [doi]
• Diversity-Based Classifier Selection for Adaptive Object TrackingIngrid Visentini, Josef Kittler, Gian Luca Foresti. 438-447 [doi]
• Ensemble Based Data Fusion for Gene Function PredictionMatteo Re, Giorgio Valentini. 448-457 [doi]
• A Cascade Multiple Classifier System for Document CategorizationJian-Wu Xu, Vartika Singh, Venu Govindaraju, Depankar Neogi. 458-467 [doi]
• Maximum Membership Scale SelectionMarco Loog, Yan Li, David M. J. Tax. 468-477 [doi]
• An Empirical Study of a Linear Regression Combiner on Multi-class Data SetsChun-Xia Zhang, Robert P. W. Duin. 478-487 [doi]
• A Study of Random Linear Oracle EnsemblesAmir Ahmad, Gavin Brown. 488-497 [doi]
• Stacking for Ensembles of Local Experts in Metabonomic ApplicationsKai Lienemann, Thomas Plötz, Gernot A. Fink. 498-508 [doi]
• Boosting Support Vector Machines SuccessfullyKai Ming Ting, Lian Zhu. 509-518 [doi]
• Manifold Learning for Multi-classifier Systems via EnsemblesMelba Crawford, Wonkook Kim. 519-528 [doi]
• When Semi-supervised Learning Meets Ensemble LearningZhi-Hua Zhou. 529-538 [doi]