Combining Pattern Classifiers

Methods and Algorithms

Ludmila I. Kuncheva

350 pages, parution le 05/08/2004

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Résumé

A unified, coherent, and expansive treatment of current classifier ensemble methods

Mail sorting, medical test reading, military target recognition, signature verification, meteorological forecast, DNA matching, fingerprint recognition. These are just a few of the areas requiring reliable, precise pattern recognition.

Although in the past, pattern recognition has focused on designing single classifiers, recently the focus has been on combining several classifiers and getting a consensus of results for greater accuracy. This interest in combining classifiers has grown astronomically in recent years, evolving into a rich and dynamic, if loosely structured, discipline. Combining Pattern Classifiers: Methods and Algorithms represents the first attempt to provide a comprehensive survey of this fast-growing field. In a clear and straightforward manner, the author provides a much-needed road map through a multifaceted and often controversial subject while effectively organizing and systematizing the current state of the art.

Covering a broad range of methodologies, algorithms, and theories, the text addresses such questions as:

Why should we combine classifiers?
What are the current approaches for building classifier ensembles?
What fusion methods can we use?
How do we measure diversity in a classifier ensemble and is diversity really a key factor to its success?

Replete with case studies and real-world applications, this groundbreaking text will be of interest to academics and researchers in the field seeking both new classification tools and new uses for the old ones.

L'auteur - Ludmila I. Kuncheva

Ludmila I. Kuncheva is a Senior Lecturer in the School of Informatics at the University of Wales, Bangor, UK.

Sommaire

Fundamentals of Pattern Recognition
Base Classifiers
Multiple Classifier Systems
Fusion of Label Outputs
Fusion of Continuous-Valued Outputs
Classifier Selection
Bagging and Boosting
Miscellanea
Theoretical Views and Results
Diversity in Classifier Ensembles
Appendix A: Equivalence Between the Averaged Disagreement Measure Dav and Kohavi-Wolpert KW
Appendix B: Matlab Code for Some Overproduce and Select Algorithms

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Wiley
Auteur(s)	Ludmila I. Kuncheva
Parution	05/08/2004
Nb. de pages	350
Format	16 x 24
Couverture	Relié
Poids	655g
Intérieur	Noir et Blanc
EAN13	9780471210788
ISBN13	978-0-471-21078-8

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