Multiple Classifier Systems

Inhaltsverzeichnis

• Future Directions.
• Semi-supervised Multiple Classifier Systems: Background and Research Directions.
• Boosting.
• Boosting GMM and Its Two Applications.
• Boosting Soft-Margin SVM with Feature Selection for Pedestrian Detection.
• Observations on Boosting Feature Selection.
• Boosting Multiple Classifiers Constructed by Hybrid Discriminant Analysis.
• Combination Methods.
• Decoding Rules for Error Correcting Output Code Ensembles.
• A Probability Model for Combining Ranks.
• EER of Fixed and Trainable Fusion Classifiers: A Theoretical Study with Application to Biometric Authentication Tasks.
• Mixture of Gaussian Processes for Combining Multiple Modalities.
• Dynamic Classifier Integration Method.
• Recursive ECOC for Microarray Data Classification.
• Using Dempster-Shafer Theory in MCF Systems to Reject Samples.
• Multiple Classifier Fusion Performance in Networked Stochastic Vector Quantisers.
• On Deriving the Second-Stage Training Set for Trainable Combiners.
• Using Independence Assumption to Improve Multimodal Biometric Fusion.
• Design Methods.
• Half-Against-Half Multi-class Support Vector Machines.
• Combining Feature Subsets in Feature Selection.
• ACE: Adaptive Classifiers-Ensemble System for Concept-Drifting Environments.
• Using Decision Tree Models and Diversity Measures in the Selection of Ensemble Classification Models.
• Ensembles of Classifiers from Spatially Disjoint Data.
• Optimising Two-Stage Recognition Systems.
• Design of Multiple Classifier Systems for Time Series Data.
• Ensemble Learning with Biased Classifiers: The Triskel Algorithm.
• Cluster-Based Cumulative Ensembles.
• Ensemble of SVMs for Incremental Learning.
• Performance Analysis.
• Design of a New Classifier Simulator.
• Evaluation of Diversity Measures for Binary Classifier Ensembles.
• Which Is the Best Multiclass SVM Method? An Empirical Study.
• Over-Fitting in Ensembles of Neural Network Classifiers Within ECOC Frameworks.
• Between Two Extremes: Examining Decompositions of the Ensemble Objective Function.
• Data Partitioning Evaluation Measures for Classifier Ensembles.
• Dynamics of Variance Reduction in Bagging and Other Techniques Based on Randomisation.
• Ensemble Confidence Estimates Posterior Probability.
• Applications.
• Using Domain Knowledge in the Random Subspace Method: Application to the Classification of Biomedical Spectra.
• An Abnormal ECG Beat Detection Approach for Long-Term Monitoring of Heart Patients Based on Hybrid Kernel Machine Ensemble.
• Speaker Verification Using Adapted User-Dependent Multilevel Fusion.
• Multi-modal Person Recognition for Vehicular Applications.
• Using an Ensemble of Classifiers to Audit a Production Classifier.
• Analysis and Modelling of Diversity Contribution to Ensemble-Based Texture Recognition Performance.
• Combining Audio-Based and Video-Based Shot Classification Systems for News Videos Segmentation.
• Designing Multiple Classifier Systems for Face Recognition.
• Exploiting Class Hierarchies for Knowledge Transfer in Hyperspectral Data.