Multiple Classifier Systems

Michal Haindl

ISBN 10: 3540724818 / ISBN 13: 9783540724810

Verlag: Springer Berlin Heidelberg, Springer Berlin Heidelberg Mai 2007, 2007

Beschreibung

Neuware -These proceedings are a record of the Multiple Classi er Systems Workshop, MCS 2007, held at the Institute of Information Theory and Automation, Czech Academy of Sciences, Prague in May 2007. Being the seventh in a well-established series of meetings providing an international forum for the discussion of issues in multiple classi er system design, the workshop achieved its objective of bringing together researchers from diverse communities (neural networks, pattern rec- nition, machine learning and statistics) concerned with this research topic. From more than 80 submissions, the Programme Committee selected 49 - pers to create an interesting scienti c programme. The special focus of MCS 2007 was on the application of multiple classi er systems in biometrics. This part- ular application area exercises all aspects of multiple classi er fusion, from - tramodal classi er combination, through con dence-based fusion, to multimodal biometric systems. The sponsorship of MCS 2007 by the European Union N- work of Excellence in Biometrics BioSecure and in Multimedia Understanding through Semantics, Computation and Learning MUSCLE and their assistance in selecting the contributions to the MCS 2007 programme consistent with this theme is gratefully acknowledged. 540 pp. Englisch.

Bestandsnummer des Verkäufers 9783540724810

Bibliografische Details

• Titel: Multiple Classifier Systems
• Verlag: Springer Berlin Heidelberg, Springer Berlin Heidelberg Mai 2007
• Erscheinungsjahr: 2007
• Sprache: Englisch
• ISBN-10: 3540724818
• ISBN-13: 9783540724810
• Einband: Taschenbuch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

This book constitutes the refereed proceedings of the 7th International Workshop on Multiple Classifier Systems, MCS 2007, held in Prague, Czech Republic in May 2007. It covers kernel-based fusion, applications, boosting, cluster and graph ensembles, feature subspace ensembles, multiple classifier system theory, intramodal and multimodal fusion of biometric experts, majority voting, and ensemble learning.