Multiple classifier systems

About this book

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