Combining Pattern Classifiers - Methods and Algorithms

Covering pattern classification methods, this book focuses on the important issue of how to combine several classifiers together in order to achieve improved recognition performance. This work is useful to those involved in the area. It includes case studies that demonstrate 'real-world' applications.

List of contents

From the contents:
Preface - Acknowledgments - Notations and Acronyms.
1. Fundamentals of Pattern Recognition.
2. Base Classifiers.
3. Multiple Classifier Systems.
4. Fusion of Label Outputs.
5. Fusion of Continuous-Valued Outputs.
6. Classifier Selection.
7. Bagging and Boosting.
8. Miscellanea.
9. Theoretical Views and Results.
10. Diversity in Classifier Ensembles.
Appendix A: Equivalence Between the Averaged Disagreement Measure D_av and Kohavi--Wolpert KW - Appendix B: Matlab Code for Some Overproduce and Select Algorithms.
References - Index

Product details

• Authors: Ludmila I. Kuncheva, Ludmilla I. Kuncheva
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Hardback
• Released: 13.07.2004
• EAN: 9780471210788
• ISBN: 978-0-471-21078-8
• No. of pages: 376
• Subject: Natural sciences, medicine, IT, technology > Technology > Electronics, electrical engineering, communications engineering