Multiple classifier systems
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About This Book
Multiple Classifier Systems: Second International Workshop, MCS 2001 Cambridge, UK, July 2–4, 2001 Proceedings<br />Author: Josef Kittler, Fabio Roli<br /> Published by Springer Berlin Heidelberg<br /> ISBN: 978-3-540-42284-6<br /> DOI: 10.1007/3-540-48219-9<br /><br />Table of Contents:<p></p><ul><li>Bagging and the Random Subspace Method for Redundant Feature Spaces
</li><li>Performance Degradation in Boosting
</li><li>A Generalized Class of Boosting Algorithms Based on Recursive Decoding Models
</li><li>Tuning Cost-Sensitive Boosting and Its Application to Melanoma Diagnosis
</li><li>Learning Classification RBF Networks by Boosting
</li><li>Data Complexity Analysis for Classifier Combination
</li><li>Genetic Programming for Improved Receiver Operating Characteristics
</li><li>Methods for Designing Multiple Classifier Systems
</li><li>Decision-Level Fusion in Fingerprint Verification
</li><li>Genetic Algorithms for Multi-classifier System Configuration: A Case Study in Character Recognition
</li><li>Combined Classification of Handwritten Digits Using the ‘Virtual Test Sample Method’
</li><li>Averaging Weak Classifiers
</li><li>Mixing a Symbolic and a Subsymbolic Expert to Improve Carcinogenicity Prediction of Aromatic Compounds
</li><li>Multiple Classifier Systems Based on Interpretable Linear Classifiers
</li><li>Least Squares and Estimation Measures via Error Correcting Output Code
</li><li>Dependence among Codeword Bits Errors in ECOC Learning Machines: An Experimental Analysis
</li><li>Information Analysis of Multiple Classifier Fusion?
</li><li>Limiting the Number of Trees in Random Forests
</li><li>Learning-Data Selection Mechanism through Neural Networks Ensemble
</li><li>A Multi-SVM Classification System</li></ul>
</li><li>Performance Degradation in Boosting
</li><li>A Generalized Class of Boosting Algorithms Based on Recursive Decoding Models
</li><li>Tuning Cost-Sensitive Boosting and Its Application to Melanoma Diagnosis
</li><li>Learning Classification RBF Networks by Boosting
</li><li>Data Complexity Analysis for Classifier Combination
</li><li>Genetic Programming for Improved Receiver Operating Characteristics
</li><li>Methods for Designing Multiple Classifier Systems
</li><li>Decision-Level Fusion in Fingerprint Verification
</li><li>Genetic Algorithms for Multi-classifier System Configuration: A Case Study in Character Recognition
</li><li>Combined Classification of Handwritten Digits Using the ‘Virtual Test Sample Method’
</li><li>Averaging Weak Classifiers
</li><li>Mixing a Symbolic and a Subsymbolic Expert to Improve Carcinogenicity Prediction of Aromatic Compounds
</li><li>Multiple Classifier Systems Based on Interpretable Linear Classifiers
</li><li>Least Squares and Estimation Measures via Error Correcting Output Code
</li><li>Dependence among Codeword Bits Errors in ECOC Learning Machines: An Experimental Analysis
</li><li>Information Analysis of Multiple Classifier Fusion?
</li><li>Limiting the Number of Trees in Random Forests
</li><li>Learning-Data Selection Mechanism through Neural Networks Ensemble
</li><li>A Multi-SVM Classification System</li></ul>
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