Multiple Classifier Systems

Multiple Classifier Systems: First International Workshop, MCS 2000 Cagliari, Italy, June 21–23, 2000 ProceedingsAuthor: Published by Springer Berlin Heidelberg ISBN: 978-3-540-67704-8 DOI: 10.1007/3-540-45014-9Table of Contents:Ensemble Methods in Machine Learning
Experiments with Classifier Combining Rules
The “Test and Select” Approach to Ensemble Combination
A Survey of Sequential Combination of Word Recognizers in Handwritten Phrase Recognition at CEDAR
Multiple Classifier Combination Methodologies for Different Output Levels
A Mathematically Rigorous Foundation for Supervised Learning
Classifier Combinations: Implementations and Theoretical Issues
Some Results on Weakly Accurate Base Learners for Boosting Regression and Classification
Complexity of Classification Problems and Comparative Advantages of Combined Classifiers
Effectiveness of Error Correcting Output Codes in Multiclass Learning Problems
Combining Fisher Linear Discriminants for Dissimilarity Representations
A Learning Method of Feature Selection for Rough Classification
Analysis of a Fusion Method for Combining Marginal Classifiers
A hybrid projection based and radial basis function architecture
Combining Multiple Classifiers in Probabilistic Neural Networks
Supervised Classifier Combination through Generalized Additive Multi-model
Dynamic Classifier Selection
Boosting in Linear Discriminant Analysis
Different Ways of Weakening Decision Trees and Their Impact on Classification Accuracy of DT Combination
Applying Boosting to Similarity Literals for Time Series Classification