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Multiple Classifier Systems

7th International Workshop, MCS 2007, Prague, Czech Republic, May 23-25, 2007, Proceedings

  • Conference proceedings
  • © 2007

Overview

Editors:
  1. Michal Haindl
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  2. Josef Kittler
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  3. Fabio Roli
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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 4472)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

Included in the following conference series:

Conference proceedings info: MCS 2007.

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About this book

These proceedings are a record of the Multiple Classi?er Systems Workshop, MCS 2007, held at the Institute of Information Theory and Automation, Czech Academy of Sciences, Prague in May 2007. Being the seventh in a well-established series of meetings providing an international forum for the discussion of issues in multiple classi?er system design, the workshop achieved its objective of bringing together researchers from diverse communities (neural networks, pattern rec- nition, machine learning and statistics) concerned with this research topic. From more than 80 submissions, the Programme Committee selected 49 - pers to create an interesting scienti?c programme. The special focus of MCS 2007 was on the application of multiple classi?er systems in biometrics. This part- ular application area exercises all aspects of multiple classi?er fusion, from - tramodal classi?er combination, through con?dence-based fusion, to multimodal biometric systems. The sponsorship of MCS 2007 by the European Union N- work of Excellence in Biometrics BioSecure and in Multimedia Understanding through Semantics, Computation and Learning MUSCLE and their assistance in selecting the contributions to the MCS 2007 programme consistent with this theme is gratefully acknowledged.

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Table of contents (51 papers)

  1. Front Matter

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  2. Kernel-Based Fusion

    1. Combining Pattern Recognition Modalities at the Sensor Level Via Kernel Fusion

      • Vadim Mottl, Alexander Tatarchuk, Valentina Sulimova, Olga Krasotkina, Oleg Seredin
      Pages 1-12

    2. The Neutral Point Method for Kernel-Based Combination of Disjoint Training Data in Multi-modal Pattern Recognition

      • David Windridge, Vadim Mottl, Alexander Tatarchuk, Andrey Eliseyev
      Pages 13-21

    3. Kernel Combination Versus Classifier Combination

      • Wan-Jui Lee, Sergey Verzakov, Robert P. W. Duin
      Pages 22-31

    4. Deriving the Kernel from Training Data

      • Stefano Merler, Giuseppe Jurman, Cesare Furlanello
      Pages 32-41

  3. Applications

    1. On the Application of SVM-Ensembles Based on Adapted Random Subspace Sampling for Automatic Classification of NMR Data

      • Kai Lienemann, Thomas Plötz, Gernot A. Fink
      Pages 42-51

    2. A New HMM-Based Ensemble Generation Method for Numeral Recognition

      • Albert Hung-Ren Ko, Robert Sabourin, Alceu de Souza Britto Jr.
      Pages 52-61

    3. Classifiers Fusion in Recognition of Wheat Varieties

      • Sarunas Raudys, Ömer Kaan Baykan, Ahmet Babalik, Vitalij Denisov, Antanas Andrius Bielskis
      Pages 62-71

    4. Multiple Classifier Methods for Offline Handwritten Text Line Recognition

      • Roman Bertolami, Horst Bunke
      Pages 72-81

    5. Applying Data Fusion Methods to Passage Retrieval in QAS

      • Hans Ulrich Christensen, Daniel Ortiz-Arroyo
      Pages 82-92

    6. A Co-training Approach for Time Series Prediction with Missing Data

      • Tawfik A. Mohamed, Neamat El Gayar, Amir F. Atiya
      Pages 93-102

    7. An Improved Random Subspace Method and Its Application to EEG Signal Classification

      • Shiliang Sun
      Pages 103-112

    8. Ensemble Learning Methods for Classifying EEG Signals

      • Shiliang Sun
      Pages 113-120

    9. Confidence Based Gating of Colour Features for Face Authentication

      • Mohammad T. Sadeghi, Samaneh Khoshrou, Josef Kittler
      Pages 121-130

    10. View-Based Eigenspaces with Mixture of Experts for View-Independent Face Recognition

      • Reza Ebrahimpour, Ehsanollah Kabir, Mohammad Reza Yousefi
      Pages 131-140

    11. Fusion of Support Vector Classifiers for Parallel Gabor Methods Applied to Face Verification

      • Ángel Serrano, Isaac Martín de Diego, Cristina Conde, Enrique Cabello, Li Bai, Linlin Shen
      Pages 141-150

    12. Serial Fusion of Fingerprint and Face Matchers

      • Gian Luca Marcialis, Fabio Roli
      Pages 151-160

  4. Boosting

    1. Boosting Lite – Handling Larger Datasets and Slower Base Classifiers

      • Lawrence O. Hall, Robert E. Banfield, Kevin W. Bowyer, W. Philip Kegelmeyer
      Pages 161-170

    2. Information Theoretic Combination of Classifiers with Application to AdaBoost

      • Julien Meynet, Jean-Philippe Thiran
      Pages 171-179

    3. Interactive Boosting for Image Classification

      • Yijuan Lu, Qi Tian, Thomas S. Huang
      Pages 180-189
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