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  • Conference proceedings
  • © 2010

Multiple Classifier Systems

9th International Workshop, MCS 2010, Cairo, Egypt, April 7-9, 2010, Proceedings

  • Fast track conference proceedings

  • State of the art papers in multiple classifier systems

  • Up to date research

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 5997)

Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)

Conference series link(s): MCS: International Workshop on Multiple Classifier Systems

Conference proceedings info: MCS 2010.

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Table of contents (34 papers)

  1. Front Matter

  2. Classifier Ensembles(I)

    1. Weighted Bagging for Graph Based One-Class Classifiers

      • Santi Seguí, Laura Igual, Jordi Vitrià
      Pages 1-10
    2. Improving Multilabel Classification Performance by Using Ensemble of Multi-label Classifiers

      • Muhammad Atif Tahir, Josef Kittler, Krystian Mikolajczyk, Fei Yan
      Pages 11-21
    3. Incremental Learning of New Classes in Unbalanced Datasets: Learn + + .UDNC

      • Gregory Ditzler, Michael D. Muhlbaier, Robi Polikar
      Pages 33-42
    4. Choosing Parameters for Random Subspace Ensembles for fMRI Classification

      • Ludmila I. Kuncheva, Catrin O. Plumpton
      Pages 54-63
  3. Classifier Ensembles(II)

    1. An Experimental Study on Ensembles of Functional Trees

      • Juan J. Rodríguez, César García-Osorio, Jesús Maudes, José Francisco Díez-Pastor
      Pages 64-73
    2. Multiple Classifier Systems under Attack

      • Battista Biggio, Giorgio Fumera, Fabio Roli
      Pages 74-83
    3. SOCIAL: Self-Organizing ClassIfier ensemble for Adversarial Learning

      • Francesco Gargiulo, Carlo Sansone
      Pages 84-93
    4. Unsupervised Change-Detection in Retinal Images by a Multiple-Classifier Approach

      • Giulia Troglio, Marina Alberti, Jón Atli Benediksson, Gabriele Moser, Sebastiano Bruno Serpico, Einar Stefánsson
      Pages 94-103
    5. A Double Pruning Algorithm for Classification Ensembles

      • Víctor Soto, Gonzalo Martínez-Muñoz, Daniel Hernández-Lobato, Alberto Suárez
      Pages 104-113
  4. Classifier Diversity

    1. “Good” and “Bad” Diversity in Majority Vote Ensembles

      • Gavin Brown, Ludmila I. Kuncheva
      Pages 124-133
    2. Multi-information Ensemble Diversity

      • Zhi-Hua Zhou, Nan Li
      Pages 134-144
  5. Classifier Selection

    1. Dynamic Selection of Ensembles of Classifiers Using Contextual Information

      • Paulo R. Cavalin, Robert Sabourin, Ching Y. Suen
      Pages 145-154
    2. Selecting Structural Base Classifiers for Graph-Based Multiple Classifier Systems

      • Wan-Jui Lee, Robert P. W. Duin, Horst Bunke
      Pages 155-164
  6. Combining Multiple Kernels

    1. A Support Kernel Machine for Supervised Selective Combining of Diverse Pattern-Recognition Modalities

      • Alexander Tatarchuk, Eugene Urlov, Vadim Mottl, David Windridge
      Pages 165-174
    2. Combining Multiple Kernels by Augmenting the Kernel Matrix

      • Fei Yan, Krystian Mikolajczyk, Josef Kittler, Muhammad Atif Tahir
      Pages 175-184
  7. Boosting and Bootstrapping

Other Volumes

  1. Multiple Classifier Systems

Keywords

  • Boosting
  • Clustering
  • Hidden Markov Model
  • Markov Model
  • algorithms
  • ants
  • bootstrap aggregating
  • classification
  • algorithm analysis and problem complexity

Editors and Affiliations

  • Center for Informatics Science, Nile University, Giza, Egypt

    Neamat Gayar

  • Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK

    Josef Kittler

  • Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy

    Fabio Roli

Bibliographic Information

Buying options

eBook USD 39.99
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
Price excludes VAT (Canada)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions