Multiple Classifier Systems: 5th International Workshop, MCS 2004, Cagliari, Italy, June 9-11, 2004. Proceedings
Author: Fabio Roli, Josef Kittler, Terry Windeatt
Published by Springer Berlin Heidelberg
ISBN: 978-3-540-22144-9
DOI: 10.1007/b98227

Table of Contents:

• Classifier Ensembles for Changing Environments
  
• A Generic Sensor Fusion Problem: Classification and Function Estimation
  
• AveBoost2: Boosting for Noisy Data
  
• Bagging Decision Multi-trees
  
• Learn++.MT: A New Approach to Incremental Learning
  
• Beyond Boosting: Recursive ECOC Learning Machines
  
• Exact Bagging with k-Nearest Neighbour Classifiers
  
• Yet Another Method for Combining Classifiers Outputs: A Maximum Entropy Approach
  
• Combining One-Class Classifiers to Classify Missing Data
  
• Combining Kernel Information for Support Vector Classification
  
• Combining Classifiers Using Dependency-Based Product Approximation with Bayes Error Rate
  
• Combining Dissimilarity-Based One-Class Classifiers
  
• A Modular System for the Classification of Time Series Data
  
• A Probabilistic Model Using Information Theoretic Measures for Cluster Ensembles
  
• Classifier Fusion Using Triangular Norms
  
• Dynamic Integration of Regression Models
  
• Dynamic Classifier Selection by Adaptive k-Nearest-Neighbourhood Rule
  
• Spectral Measure for Multi-class Problems
  
• The Relationship between Classifier Factorisation and Performance in Stochastic Vector Quantisation
  
• A Method for Designing Cost-Sensitive ECOC