Multiple Classifier Systems
Subtitle: First International Workshop, MCS 2000 Cagliari, Italy, June 21-23, 2000 Proceedings (Lecture Notes in Computer Science)
Contributors: Josef Kittler (Editor); Fabio Roli (Editor)
Edition: 1 edition
Publisher: Springer
Published: July 26, 2000
Language: eng
Format: Paperback
Number of pages: 404
ISBN-13: 9783540677048
Subjects: Artificial intelligence; Image processing; Pattern Recognition; General; Science/Mathematics; Computers; Medical / Nursing; Neural networks (Computer science); Neural networks (Computer scie; Machine Learning; Artificial Intelligence - General; Computer Vision; Computers / Computer Vision; Pattern perception; Data Processing - General; Congresses
First sentence: Consider the standard supervised learning problem.
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>