Multiple Classifier Systems

Multiple Classifier Systems: 5th International Workshop, MCS 2004, Cagliari, Italy, June 9-11, 2004. ProceedingsAuthor: Fabio Roli, Josef Kittler, Terry Windeatt Published by Springer Berlin Heidelberg ISBN: 978-3-540-22144-9 DOI: 10.1007/b98227Table 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