多分类器系统 Multiple classifier systems

2002-12  

1 edition (2002年8月1日)  

Fabio Roli  

335  

This book constitutes the refereed proceedings of the Third International Workshop on Multiple Classifier Systems, MCS 2002, held in Cagliari, Italy, in June 2002.The 29 revised full papers presented together with three invited papers were carefully reviewed and selected for inclusion in the volume. The papers are organized in topical sections on bagging and boosting, ensemble learning and neural networks, design methodologies, combination strategies, analysis and performance evaluation, and applications.

Invited Papers　Multiclassifier Systems: Back to the ~ture　Support Vector Machines, Kernel Logistic Regression and Boosting　Multiple Classification Systems in the Context of Feature Extraction and SelectionBagging 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 InverseEnsemble 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 MiningDesign 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 SystemsCombination Strategies　Trainable Multiple Classifier Schemes for Handwritten Character Recognition　Generating Classifiers Ensembles from Multiple Prototypes and Its Application to Handwriting 　Recognition　Adaptive Feature Spaces for Land Cover Classification with Limited Ground Truth　Stacking with Multi-response Model Trees　On Combining One-Class Classifiers for Image Database RetrievalAnalysis and Performance Evaluation　Bias-Variance Analysis and Ensembles of SVM　An Experimental Comparison of Fixed and Trained Rules for Crisp Classifiers Outputs……ApplicationsAuthor Index

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