Multiple classifier systems

Multiple Classifier Systems: First International Workshop, MCS 2000 Cagliari, Italy, June 21–23, 2000 Proceedings
Author:
Published by Springer Berlin Heidelberg
ISBN: 978-3-540-67704-8
DOI: 10.1007/3-540-45014-9

Table 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