Artificial Neural Networks in Pattern Recognition Third IAPR Wor
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Subseries of Lecture Notes in Computer Science
5064
Lionel Prevost Simone Marinai Friedhelm Schwenker (Eds.)
Artificial Neural Networks in Pattern Recognition Third IAPR Workshop, ANNPR 2008 Paris, France, July 2-4, 2008 Proceedings
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Series Editors Randy Goebel, University of Alberta, Edmonton, Canada Jörg Siekmann, University of Saarland, Saarbrücken, Germany Wolfgang Wahlster, DFKI and University of Saarland, Saarbrücken, Germany Volume Editors Lionel Prevost ISIR, Université Pierre et Marie Curie - Paris 6 75252 Paris, France E-mail: [email protected] Simone Marinai Dipartimento di Sistemi e Informatica Università di Firenze 50139 Firenze, Italy E-mail: [email protected] Friedhelm Schwenker Institute of Neural Information Processing University of Ulm 89069 Ulm, Germany E-mail: [email protected]
Library of Congress Control Number: 2008929582
CR Subject Classification (1998): I.2.6, I.2, I.5, I.4, H.3, F.2.2, J.3 LNCS Sublibrary: SL 7 – Artificial Intelligence ISSN ISBN-10 ISBN-13
0302-9743 3-540-69938-4 Springer Berlin Heidelberg New York 978-3-540-69938-5 Springer Berlin Heidelberg New York
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Preface
The Third IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2008, was held at Pierre and Marie Curie University in Paris (France), July 2–4, 2008. The workshop was organized by the Technical Committee on Neural Networks and Computational Intelligence (TC3) that is one of the 20 TCs of the International Association for Pattern Recognition (IAPR). The scope of TC3 includes computational intelligence approaches, such as fuzzy systems, evolutionary computing and artificial neural networks and their use in various pattern recognition applications. ANNPR 2008 followed the success of the previous workshops: ANNPR 2003 held at the University of Florence (Italy) and ANPPR 2006 held at Reisensburg Castle, University of Ulm (Germany). All the workshops featured a single-track program including both oral sessions and posters with a focus on active participation from every participant. In recent years, the field of neural networks has matured considerably in both methodology and real-world applications. As reflect
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