Multiple Classifier Systems
Subtitle: 5th International Workshop, MCS 2004, Cagliari, Italy, June 9-11, 2004, Proceedings (Lecture Notes in Computer Science)
Contributors: Fabio Roli (Editor); Josef Kittler (Editor); Terry Windeatt (Editor)
Edition: 1 edition
Publisher: Springer
Published: July 27, 2004
Language: eng
Format: Paperback
Number of pages: 386
ISBN-13: 9783540221449
Subjects: Machine learning; Computers; Computer Books: General; Computers - General Information; Artificial Intelligence - General; Computer Science; Computer Vision; Computers / Computer Vision; algorithmic learning; classification; classifier systems; clustering; document analysis; image analysis; learning classifier systems; General; Congresses; Neural networks (Computer science); Pattern perception
First sentence: Most of the current research in multiple classifier systems is devoted to static environments.
Description: Multiple Classifier Systems: 5th International Workshop, MCS 2004, Cagliari, Italy, June 9-11, 2004. Proceedings<br />Author: Fabio Roli, Josef Kittler, Terry Windeatt<br /> Published by Springer Berlin Heidelberg<br /> ISBN: 978-3-540-22144-9<br /> DOI: 10.1007/b98227<br /><br />Table of Contents:<p></p><ul><li>Classifier Ensembles for Changing Environments
</li><li>A Generic Sensor Fusion Problem: Classification and Function Estimation
</li><li>AveBoost2: Boosting for Noisy Data
</li><li>Bagging Decision Multi-trees
</li><li>Learn++.MT: A New Approach to Incremental Learning
</li><li>Beyond Boosting: Recursive ECOC Learning Machines
</li><li>Exact Bagging with k-Nearest Neighbour Classifiers
</li><li>Yet Another Method for Combining Classifiers Outputs: A Maximum Entropy Approach
</li><li>Combining One-Class Classifiers to Classify Missing Data
</li><li>Combining Kernel Information for Support Vector Classification
</li><li>Combining Classifiers Using Dependency-Based Product Approximation with Bayes Error Rate
</li><li>Combining Dissimilarity-Based One-Class Classifiers
</li><li>A Modular System for the Classification of Time Series Data
</li><li>A Probabilistic Model Using Information Theoretic Measures for Cluster Ensembles
</li><li>Classifier Fusion Using Triangular Norms
</li><li>Dynamic Integration of Regression Models
</li><li>Dynamic Classifier Selection by Adaptive k-Nearest-Neighbourhood Rule
</li><li>Spectral Measure for Multi-class Problems
</li><li>The Relationship between Classifier Factorisation and Performance in Stochastic Vector Quantisation
</li><li>A Method for Designing Cost-Sensitive ECOC</li></ul>