Multiple classifier systems
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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:
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
