Multiple Classifier Systems

5th International Workshop, MCS 2004, Cagliari, Italy, June 9-11, 2004. Proceedings

  • Fabio Roli
  • Josef Kittler
  • Terry Windeatt
Conference proceedings MCS 2004
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3077)

Table of contents

  1. Front Matter
  2. Invited Papers

  3. Bagging and Boosting

    1. Nikunj C. Oza
      Pages 31-40
    2. Vicent Estruch, César Ferri, José Hernández-Orallo, Maria José Ramírez-Quintana
      Pages 41-51
    3. Michael Muhlbaier, Apostolos Topalis, Robi Polikar
      Pages 52-61
    4. Elizabeth Tapia, José C. González, Alexander Hütermann, Javier García
      Pages 62-71
    5. Bruno Caprile, Stefano Merler, Cesare Furlanello, Giuseppe Jurman
      Pages 72-81
  4. Combination Methods

    1. Piotr Juszczak, Robert P. W. Duin
      Pages 92-101
    2. Isaac Martín de Diego, Javier M. Moguerza, Alberto Muñoz
      Pages 102-111
    3. Elżbieta Pękalska, Marina Skurichina, Robert P. W. Duin
      Pages 122-133
    4. Lei Chen, Mohamed Kamel, Ju Jiang
      Pages 134-143
    5. Hanan Ayad, Otman Basir, Mohamed Kamel
      Pages 144-153
    6. Piero Bonissone, Kai Goebel, Weizhong Yan
      Pages 154-163
    7. Niall Rooney, David Patterson, Sarab Anand, Alexey Tsymbal
      Pages 164-173
    8. Luca Didaci, Giorgio Giacinto
      Pages 174-183
  5. Design Methods

About these proceedings

Introduction

The fusion of di?erent information sourcesis a persistent and intriguing issue. It hasbeenaddressedforcenturiesinvariousdisciplines,includingpoliticalscience, probability and statistics, system reliability assessment, computer science, and distributed detection in communications. Early seminal work on fusion was c- ried out by pioneers such as Laplace and von Neumann. More recently, research activities in information fusion have focused on pattern recognition. During the 1990s,classi?erfusionschemes,especiallyattheso-calleddecision-level,emerged under a plethora of di?erent names in various scienti?c communities, including machine learning, neural networks, pattern recognition, and statistics. The d- ferent nomenclatures introduced by these communities re?ected their di?erent perspectives and cultural backgrounds as well as the absence of common forums and the poor dissemination of the most important results. In 1999, the ?rst workshop on multiple classi?er systems was organized with the main goal of creating a common international forum to promote the diss- ination of the results achieved in the diverse communities and the adoption of a common terminology, thus giving the di?erent perspectives and cultural ba- grounds some concrete added value. After ?ve meetings of this workshop, there is strong evidence that signi?cant steps have been made towards this goal. - searchers from these diverse communities successfully participated in the wo- shops, and world experts presented surveys of the state of the art from the perspectives of their communities to aid cross-fertilization.

Keywords

Bagging Boosting Performance Random Forest classification classifier systems cognition learning learning classifier systems machine learning multiple classifier systems neural networks speech recognition statistical learning verification

Editors and affiliations

  • Fabio Roli
    • 1
  • Josef Kittler
    • 2
  • Terry Windeatt
    • 3
  1. 1.Department of Electrical and Electronic Engineering, Piazza d’ArmiUniversity of CagliariCagliariItaly
  2. 2.Centre for Vision, Speech and Signal ProcessingUniversity of SurreyGuildfordUK
  3. 3.Centre for Vision, Speech and Signal Proc (CVSSP)University of SurreyGuildfordUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/b98227
  • Copyright Information Springer-Verlag Berlin Heidelberg 2004
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-22144-9
  • Online ISBN 978-3-540-25966-4
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349