Advanced Lectures on Machine Learning ML Summer Schools 2003, Ca
Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in Febr
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Subseries of Lecture Notes in Computer Science
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Olivier Bousquet Ulrike von Luxburg Gunnar Rätsch (Eds.)
Advanced Lectures on Machine Learning ML Summer Schools 2003 Canberra, Australia, February 2-14, 2003 Tübingen, Germany, August 4-16, 2003 Revised Lectures
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Series Editors Jaime G. Carbonell, Carnegie Mellon University, Pittsburgh, PA, USA Jörg Siekmann, University of Saarland, Saarbrücken, Germany Volume Editors Olivier Bousquet Ulrike von Luxburg Max Planck Institute for Biological Cybernetics Spemannstr. 38, 72076 Tübingen, Germany E-mail: {bousquet, ule}@tuebingen.mpg.de Gunnar Rätsch Fraunhofer FIRST Kekuléstr. 7, 10245 Berlin, Germany and Max Planck Institute for Biological Cybernetics Spemannstr. 38, 72076 Tübingen, Germany E-mail: [email protected]
Library of Congress Control Number: 2004111357
CR Subject Classification (1998): I.2.6, I.2, F.1, F.2, I.5 ISSN 0302-9743 ISBN 3-540-23122-6 Springer Berlin Heidelberg New York This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. Springer is a part of Springer Science+Business Media springeronline.com © Springer-Verlag Berlin Heidelberg 2004 Printed in Germany Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, India Printed on acid-free paper SPIN: 11322894 06/3142 543210
Preface
Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a series of summer schools was started in February 2002. One year later two more of such summer schools were held, one at the Australian National University in Canberra, Australia, and the other one in the Max-Planck Institute for Biological Cybernetics, in T¨ ubingen, Germany. The current book contains a collection of main talks held during those two summer schools, presented as tutorial chapters on topics such as Pattern Recognition, Bayesian Inference, Unsupervised Learning and Statistical Learning Theory. The papers provide an in-depth overview of these exciting new areas, contain a large set of references, and thereby provide the interested reader with further information to start or to pursue his own research in these directions. Complementary to the book, photos and slides of the presentations can be obtained at http://mlg.anu.edu.au/summer2003 and http://www.irccyn.ec-nantes.fr/mlschool/mlss03/home03.php. The general entry point for past and future Machine Learning Summer
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