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Classification - the Ubiquitous Challenge

Proceedings of the 28th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Dortmund, March 9-11, 2004

  • Conference proceedings
  • © 2005

Overview

Editors:
  1. Claus Weihs

    1. Fachbereich Statistik, Universität Dortmund, Dortmund

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  2. Wolfgang Gaul

    1. Institut für Entscheidungstheorie und Unternehmensforschung, Universität Karlsruhe (TH), Karlsruhe

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Table of contents (82 papers)

  1. Front Matter

    Pages I-XX
    Download chapter PDF

  2. (Semi-) Plenary Presentations

    1. Classification and Data Mining in Musicology

      • Jan Beran
      Pages 3-10

    2. Bayesian Mixed Membership Models for Soft Clustering and Classification

      • Elena A. Erosheva, Stephen E. Fienberg
      Pages 11-26

    3. Predicting Protein Secondary Structure with Markov Models

      • Paul Fischer, Simon Larsen, Claus Thomsen
      Pages 27-33

    4. Milestones in the History of Data Visualization: A Case Study in Statistical Historiography

      • Michael Friendly
      Pages 34-52

    5. Quantitative Text Typology: The Impact of Word Length

      • Peter Grzybek, Ernst Stadlober, Emmerich Kelih, Gordana Antić
      Pages 53-64

    6. Cluster Ensembles

      • Kurt Hornik
      Pages 65-72

    7. Bootstrap Confidence Intervals for Three-way Component Methods

      • Henk A.L. Kiers
      Pages 73-84

    8. Organising the Knowledge Space for Software Components

      • Claus Pahl
      Pages 85-96

    9. Multimedia Pattern Recognition in Soccer Video Using Time Intervals

      • Cees G.M. Snoek, Marcel Worring
      Pages 97-108

    10. Quantitative Assessment of the Responsibility for the Disease Load in a Population

      • Wolfgang Uter, Olaf Gefeller
      Pages 109-117

  3. Classification and Data Analysis

    1. Bootstrapping Latent Class Models

      • José G. Dias
      Pages 121-128

    2. Dimensionality of Random Subspaces

      • Eugeniusz Gatnar
      Pages 129-136

    3. Two-stage Classification with Automatic Feature Selection for an Industrial Application

      • Sören Hader, Fred A. Hamprecht
      Pages 137-144

    4. Bagging, Boosting and Ordinal Classification

      • Klaus Hechenbichler, Gerhard Tutz
      Pages 145-152

    5. A Method for Visual Cluster Validation

      • Christian Hennig
      Pages 153-160

    6. Empirical Comparison of Boosting Algorithms

      • Riadh Khanchel, Mohamed Limam
      Pages 161-167

    7. Iterative Majorization Approach to the Distance-based Discriminant Analysis

      • Serhiy Kosinov, Stéphane Marchand-Maillet, Thierry Pun
      Pages 168-175

    8. An Extension of the CHAID Tree-based Segmentation Algorithm to Multiple Dependent Variables

      • Jay Magidson, Jeroen K. Vermunt
      Pages 176-183

    9. Expectation of Random Sets and the ‘Mean Values’ of Interval Data

      • Ole Nordhoff
      Pages 184-191
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Editors and Affiliations

  • Fachbereich Statistik, Universität Dortmund, Dortmund

    Claus Weihs

  • Institut für Entscheidungstheorie und Unternehmensforschung, Universität Karlsruhe (TH), Karlsruhe

    Wolfgang Gaul

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