Intelligent Data Engineering and Automated Learning - IDEAL 2005 6th
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Editorial Board David Hutchison Lancaster University, UK Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M. Kleinberg Cornell University, Ithaca, NY, USA Friedemann Mattern ETH Zurich, Switzerland John C. Mitchell Stanford University, CA, USA Moni Naor Weizmann Institute of Science, Rehovot, Israel Oscar Nierstrasz University of Bern, Switzerland C. Pandu Rangan Indian Institute of Technology, Madras, India Bernhard Steffen University of Dortmund, Germany Madhu Sudan Massachusetts Institute of Technology, MA, USA Demetri Terzopoulos New York University, NY, USA Doug Tygar University of California, Berkeley, CA, USA Moshe Y. Vardi Rice University, Houston, TX, USA Gerhard Weikum Max-Planck Institute of Computer Science, Saarbruecken, Germany
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Marcus Gallagher James Hogan Frederic Maire (Eds.)
Intelligent Data Engineering and Automated Learning – IDEAL 2005 6th International Conference Brisbane, Australia, July 6-8, 2005 Proceedings
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Volume Editors Marcus Gallagher University of Queensland School of Information Technology and Electrical Engineering Brisbane Qld 4072, Australia E-mail: [email protected] James Hogan Frederic Maire Queensland University of Technology School of Software Engineering and Data Communications GPO Box 2434, Brisbane Qld 4001, Australia E-mail: {j.hogan,f.maire}@qut.edu.au
Library of Congress Control Number: 2005928541 CR Subject Classification (1998): H.2.8, F.2.2, I.2, F.4, K.4.4, H.3, H.4 ISSN ISBN-10 ISBN-13
0302-9743 3-540-26972-X Springer Berlin Heidelberg New York 978-3-540-26972-4 Springer Berlin Heidelberg New York
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Preface
The ongoing success of the Intelligent Data Engineering and Automated Learning (IDEAL) conference series reflects the continuing need for intelligent approaches to understanding relationships in the massive data sets which confront the modern researcher. From its origins in Hong Kong in 1998, this focus upon the nature of the data has been the unifying theme of the conference, allowing it to become a key forum for researchers to present novel approaches to data engineering and learning, and to provide a particularly valuable opportunity for cro
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