Kernel-Based Fusion -- Combining Pattern Recognition Modalities at the Sensor Level Via Kernel Fusion -- The Neutral Point Method for Kernel-Based Combination of Disjoint Training Data in Multi-modal Pattern Recognition -- Kernel Combination Versus Classifier Combination -- Deriving the Kernel from Training Data -- Applications -- On the Application of SVM-Ensembles Based on Adapted Random Subspace Sampling for Automatic Classification of NMR Data -- A New HMM-Based Ensemble Generation Method for Numeral Recognition -- Classifiers Fusion in Recognition of Wheat Varieties -- Multiple Classifier Methods for Offline Handwritten Text Line Recognition -- Applying Data Fusion Methods to Passage Retrieval in QAS -- A Co-training Approach for Time Series Prediction with Missing Data -- An Improved Random Subspace Method and Its Application to EEG Signal Classification -- Ensemble Learning Methods for Classifying EEG Signals -- Confidence Based Gating of Colour Features for Face Authentication -- View-Based Eigenspaces with Mixture of Experts for View-Independent Face Recognition -- Fusion of Support Vector Classifiers for Parallel Gabor Methods Applied to Face Verification -- Serial Fusion of Fingerprint and Face Matchers -- Boosting -- Boosting Lite – Handling Larger Datasets and Slower Base Classifiers -- Information Theoretic Combination of Classifiers with Application to AdaBoost -- Interactive Boosting for Image Classification -- Cluster and Graph Ensembles -- Group-Induced Vector Spaces -- Selecting Diversifying Heuristics for Cluster Ensembles -- Unsupervised Texture Segmentation Using Multiple Segmenters Strategy -- Classifier Ensembles for Vector Space Embedding of Graphs -- Cascading for Nominal Data -- Feature Subspace Ensembles -- A Combination of Sample Subsets and Feature Subsets in One-Against-Other Classifiers -- Random Feature Subset Selection for Ensemble Based Classification of Data with Missing Features -- Feature Subspace Ensembles: A Parallel Classifier Combination Scheme Using Feature Selection -- Stopping Criteria for Ensemble-Based Feature Selection -- Multiple Classifier System Theory -- On Rejecting Unreliably Classified Patterns -- Bayesian Analysis of Linear Combiners -- Applying Pairwise Fusion Matrix on Fusion Functions for Classifier Combination -- Modelling Multiple-Classifier Relationships Using Bayesian Belief Networks -- Classifier Combining Rules Under Independence Assumptions -- Embedding Reject Option in ECOC Through LDPC Codes -- Intramodal and Multimodal Fusion of Biometric Experts -- On Combination of Face Authentication Experts by a Mixture of Quality Dependent Fusion Classifiers -- Index Driven Combination of Multiple Biometric Experts for AUC Maximisation -- Q???stack: Uni- and Multimodal Classifier Stacking with Quality Measures -- Reliability-Based Voting Schemes Using Modality-Independent Features in Multi-classifier Biometric Authentication -- Optimal Classifier Combination Rules for Verification and Identification Systems -- Majority Voting -- Exploiting Diversity in Ensembles: Improving the Performance on Unbalanced Datasets -- On the Diversity-Performance Relationship for Majority Voting in Classifier Ensembles -- Hierarchical Behavior Knowledge Space -- Ensemble Learning -- A New Dynamic Ensemble Selection Method for Numeral Recognition -- Ensemble Learning in Linearly Combined Classifiers Via Negative Correlation -- Naïve Bayes Ensembles with a Random Oracle -- An Experimental Study on Rotation Forest Ensembles -- Cooperative Coevolutionary Ensemble Learning -- Robust Inference in Bayesian Networks with Application to Gene Expression Temporal Data -- An Ensemble Approach for Incremental Learning in Nonstationary Environments -- Invited Papers -- Multiple Classifier Systems in Remote Sensing: From Basics to Recent Developments -- Biometric Person Authentication Is a Multiple Classifier Problem.