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Machine Learning and Data Mining in Pattern Recognition

First International Workshop, MLDM’99 Leipzig, Germany, September 16–18, 1999 Proceedings

  • Petra Perner
  • Maria Petrou
Conference proceedings MLDM 1999

Part of the Lecture Notes in Computer Science book series (LNCS, volume 1715)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 1715)

Table of contents

  1. Front Matter
    Pages I-VIII
  2. Invited Papers

    1. M. Petrou
      Pages 1-12
    2. Se June Hong, Sholom M. Weiss
      Pages 13-20
  3. Neural Networks Applied to Image Processing and Recognition

    1. Atsushi Imiya, Kazuhiko Kawamoto
      Pages 36-50
    2. Mariofanna Milanova, Paulo E. M. Almeida, Jun Okamoto Jr., Marcelo Godoy Simões
      Pages 51-63
  4. Learning in Image Pre-Processing and Segmentation

    1. M. Reczko, D. A. Karras, V. Mertzios, D. Graveron-Demilly, D. van Ormondt
      Pages 75-86
  5. Image Retrieval

  6. Classification and Image Interpretation

    1. Carsten Jacobsen, Uwe Zscherpel, Petra Perner
      Pages 144-158
  7. Symbolic Learning and Neural Networks in Document Processing

    1. Oronzo Altamura, Floriana Esposito, Francesca A. Lisi, Donato Malerba
      Pages 159-173
    2. Tomáš Beran, Tomáš Macek
      Pages 174-179
  8. Data Mining

  9. Back Matter
    Pages 217-217

About these proceedings

Introduction

The field of machine learning and data mining in connection with pattern recognition enjoys growing popularity and attracts many researchers. Automatic pattern recognition systems have proven successful in many applications. The wide use of these systems depends on their ability to adapt to changing environmental conditions and to deal with new objects. This requires learning capabilities on the parts of these systems. The exceptional attraction of learning in pattern recognition lies in the specific data themselves and the different stages at which they get processed in a pattern recognition system. This results a specific branch within the field of machine learning. At the workshop, were presented machine learning approaches for image pre-processing, image segmentation, recognition and interpretation. Machine learning systems were shown on applications such as document analysis and medical image analysis. Many databases are developed that contain multimedia sources such as images, measurement protocols, and text documents. Such systems should be able to retrieve these sources by content. That requires specific retrieval and indexing strategies for images and signals. Higher quality database contents can be achieved if it were possible to mine these databases for their underlying information. Such mining techniques have to consider the specific characteristic of the image sources. The field of mining multimedia databases is just starting out. We hope that our workshop can attract many other researchers to this subject.

Keywords

Data Mining Image Processing Intelligent Data Analysis Pattern Recognition classification learning machine learning

Editors and affiliations

  • Petra Perner
    • 1
  • Maria Petrou
    • 2
  1. 1.Institut für Bildverarbeitung und angewandte InformatikLeipzigGermany
  2. 2.School of Electronic Engineering, Information Technology and MathematicsUniversity of SurreyGuilfordUK

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-48097-8
  • Copyright Information Springer-Verlag Berlin Heidelberg 1999
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-66599-1
  • Online ISBN 978-3-540-48097-6
  • Series Print ISSN 0302-9743
  • Buy this book on publisher's site
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