Multiple Classifier Systems

Second International Workshop, MCS 2001 Cambridge, UK, July 2–4, 2001 Proceedings

  • Josef Kittler
  • Fabio Roli
Conference proceedings MCS 2001
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2096)

Table of contents

  1. Front Matter
    Pages I-XII
  2. Bagging and Boosting

    1. Marina Skurichina, Robert P. W. Duin
      Pages 1-10
    2. Jeevani Wickramaratna, Sean Holden, Bernard Buxton
      Pages 11-21
    3. Elizabeth Tapia, José C. González, Julio Villena
      Pages 22-31
    4. Stefano Merler, Cesare Furlanello, Barbara Larcher, Andrea Sboner
      Pages 32-42
    5. Juan J. Rodríguez Diez, Carlos J. Alonso González
      Pages 43-52
  3. MCS Design Methodology

    1. Fabio Roli, Giorgio Giacinto, Gianni Vernazza
      Pages 78-87
    2. Salil Prabhakar, Anil K. Jain
      Pages 88-98
    3. Dechang Chen, Jian Liu
      Pages 119-125
    4. Giuseppina Gini, Marco Lorenzini, Emilio Benfenati, Raffaella Brambilla, Luca Malvé
      Pages 126-135
  4. Ensemble Classifiers

    1. David J. Hand, Niall M. Adams, Mark G. Kelly
      Pages 136-147
    2. Jiří Grim, Josef Kittler, Pavel Pudil, Petr Somol
      Pages 168-177
    3. Patrice Latinne, Olivier Debeir, Christine Decaestecker
      Pages 178-187
    4. Pitoyo Hartono, Shuji Hashimoto
      Pages 188-197

About these proceedings

Introduction

Driven by the requirements of a large number of practical and commercially - portant applications, the last decade has witnessed considerable advances in p- tern recognition. Better understanding of the design issues and new paradigms, such as the Support Vector Machine, have contributed to the development of - proved methods of pattern classi cation. However, while any performance gains are welcome, and often extremely signi cant from the practical point of view, it is increasingly more challenging to reach the point of perfection as de ned by the theoretical optimality of decision making in a given decision framework. The asymptoticity of gains that can be made for a single classi er is a re?- tion of the fact that any particular design, regardless of how good it is, simply provides just one estimate of the optimal decision rule. This observation has motivated the recent interest in Multiple Classi er Systems , which aim to make use of several designs jointly to obtain a better estimate of the optimal decision boundary and thus improve the system performance. This volume contains the proceedings of the international workshop on Multiple Classi er Systems held at Robinson College, Cambridge, United Kingdom (July 2{4, 2001), which was organized to provide a forum for researchers in this subject area to exchange views and report their latest results.

Keywords

Algorithmic Learning Bagging Boosting Classification Classifier SYstems Document Analysis Image ANalysis Machine Learning Multiple Classifier Systems Neural Networks Pattern Recognition Remote Sensing Time Series Analysis

Editors and affiliations

  • Josef Kittler
    • 1
  • Fabio Roli
    • 2
  1. 1.Centre for Vision, Speech and Signal ProcessingUniversity of SurreyGuildfordUK
  2. 2.Department of Electrical and Electronic EngineeringUniversity of CagliariCagliariItaly

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-48219-9
  • Copyright Information Springer-Verlag Berlin Heidelberg 2001
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-42284-6
  • Online ISBN 978-3-540-48219-2
  • Series Print ISSN 0302-9743