Invited Papers.- Multiclassifier Systems: Back to the Future.- Support Vector Machines, Kernel Logistic Regression and Boosting.- Multiple Classification Systems in the Context of Feature Extraction and Selection.- Bagging and Boosting.- Boosted Tree Ensembles for Solving Multiclass Problems.- Distributed Pasting of Small Votes.- Bagging and Boosting for the Nearest Mean Classifier: Effects of Sample Size on Diversity and Accuracy.- Highlighting Hard Patterns via AdaBoost Weights Evolution.- Using Diversity with Three Variants of Boosting: Aggressive, Conservative, and Inverse.- Ensemble Learning and Neural Networks.- Multistage Neural Network Ensembles.- Forward and Backward Selection in Regression Hybrid Network.- Types of Multinet System.- Discriminant Analysis and Factorial Multiple Splits in Recursive Partitioning for Data Mining.- Design Methodologies.- New Measure of Classifier Dependency in Multiple Classifier Systems.- A Discussion on the Classifier Projection Space for Classifier Combining.- On the General Application of the Tomographic Classifier Fusion Methodology.- Post-processing of Classifier Outputs in Multiple Classifier Systems.- Combination Strategies.- Trainable Multiple Classifier Schemes for Handwritten Character Recognition.- Generating Classifier Ensembles from Multiple Prototypes and Its Application to Handwriting Recognition.- Adaptive Feature Spaces for Land Cover Classification with Limited Ground Truth Data.- Stacking with Multi-response Model Trees.- On Combining One-Class Classifiers for Image Database Retrieval.- Analysis and Performance Evaluation.- Bias-Variance Analysis and Ensembles of SVM.- An Experimental Comparison of Fixed and Trained Fusion Rules for Crisp Classifier Outputs.- Reduction of the Boasting Bias of Linear Experts.-Analysis of Linear and Order Statistics Combiners for Fusion of Imbalanced Classifiers.- Applications.- Boosting and Classification of Electronic Nose Data.- Content-Based Classification of Digital Photos.- Classifier Combination for In Vivo Magnetic Resonance Spectra of Brain Tumours.- Combining Classifiers of Pesticides Toxicity through a Neuro-fuzzy Approach.- A Multi-expert System for Movie Segmentation.- Decision Level Fusion of Intramodal Personal Identity Verification Experts.- An Experimental Comparison of Classifier Fusion Rules for Multimodal Personal Identity Verification Systems.
Inhaltsverzeichnis
Invited Papers. - Multiclassifier Systems: Back to the Future. - Support Vector Machines, Kernel Logistic Regression and Boosting. - Multiple Classification Systems in the Context of Feature Extraction and Selection. - Bagging and Boosting. - Boosted Tree Ensembles for Solving Multiclass Problems. - Distributed Pasting of Small Votes. - Bagging and Boosting for the Nearest Mean Classifier: Effects of Sample Size on Diversity and Accuracy. - Highlighting Hard Patterns via AdaBoost Weights Evolution. - Using Diversity with Three Variants of Boosting: Aggressive, Conservative, and Inverse. - Ensemble Learning and Neural Networks. - Multistage Neural Network Ensembles. - Forward and Backward Selection in Regression Hybrid Network. - Types of Multinet System. - Discriminant Analysis and Factorial Multiple Splits in Recursive Partitioning for Data Mining. - Design Methodologies. - New Measure of Classifier Dependency in Multiple Classifier Systems. - A Discussion on the Classifier Projection Space for Classifier Combining. - On the General Application of the Tomographic Classifier Fusion Methodology. - Post-processing of Classifier Outputs in Multiple Classifier Systems. - Combination Strategies. - Trainable Multiple Classifier Schemes for Handwritten Character Recognition. - Generating Classifier Ensembles from Multiple Prototypes and Its Application to Handwriting Recognition. - Adaptive Feature Spaces for Land Cover Classification with Limited Ground Truth Data. - Stacking with Multi-response Model Trees. - On Combining One-Class Classifiers for Image Database Retrieval. - Analysis and Performance Evaluation. - Bias Variance Analysis and Ensembles of SVM. - An Experimental Comparison of Fixed and Trained Fusion Rules for Crisp Classifier Outputs. - Reduction of the Boasting Bias of Linear Experts. -Analysis of Linear and Order Statistics Combiners for Fusion of Imbalanced Classifiers. - Applications. - Boosting and Classification of Electronic Nose Data. - Content-Based Classification of Digital Photos. - Classifier Combination for In Vivo Magnetic Resonance Spectra of Brain Tumours. - Combining Classifiers of Pesticides Toxicity through a Neuro-fuzzy Approach. - A Multi-expert System for Movie Segmentation. - Decision Level Fusion of Intramodal Personal Identity Verification Experts. - An Experimental Comparison of Classifier Fusion Rules for Multimodal Personal Identity Verification Systems.
