Multiple classifier systems

By Josef Kittler, Fabio Roli

Subjects: Congresses, Neural networks (Computer science), Neural networks (computer science), Machine learning, Pattern perception

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