Machine Learning and Data Mining in Pattern Recognition

4th International Conference, MLDM 2005, Leipzig, Germany, July 9-11, 2005. Proceedings

  • Petra Perner
  • Atsushi Imiya

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3587)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 3587)

Table of contents

  1. Front Matter
  2. Classification and Model Estimation

    1. J. Ko, E. Kim
      Pages 1-10
    2. Anamika Gupta, Naveen Kumar, Vasudha Bhatnagar
      Pages 11-20
    3. Yanmin Sun, A. K. C. Wong, Yang Wang
      Pages 21-30
    4. Baibo Zhang, Changshui Zhang
      Pages 31-41
    5. Vadim Mottl, Olga Krasotkina, Oleg Seredin, Ilya Muchnik
      Pages 52-61
  3. Neural Methods

    1. Ranadhir Ghosh, Moumita Ghosh, John Yearwood, Adil Bagirov
      Pages 71-79
    2. Johan Huysmans, Bart Baesens, Jan Vanthienen
      Pages 80-89
  4. Subspace Methods

    1. Ichigaku Takigawa, Mineichi Kudo, Atsuyoshi Nakamura
      Pages 90-99
    2. Laurent Candillier, Isabelle Tellier, Fabien Torre, Olivier Bousquet
      Pages 100-109
    3. Gero Szepannek, Karsten Luebke, Claus Weihs
      Pages 110-119
  5. Clustering: Basics

    1. Christoph F. Eick, Alain Rouhana, Abraham Bagherjeiran, Ricardo Vilalta
      Pages 120-131
    2. Robert Haralick, Rave Harpaz
      Pages 132-141
    3. Vladimir Nikulin, Alex J. Smola
      Pages 142-152
    4. Silke Jänichen, Petra Perner
      Pages 153-162
  6. Applications of Clustering

    1. William Sia, Mihai M. Lazarescu
      Pages 163-173
    2. Giorgio Giacinto, Roberto Perdisci, Fabio Roli
      Pages 184-193

About these proceedings

Introduction

We met again in front of the statue of Gottfried Wilhelm von Leibniz in the city of Leipzig. Leibniz, a famous son of Leipzig, planned automatic logical inference using symbolic computation, aimed to collate all human knowledge. Today, artificial intelligence deals with large amounts of data and knowledge and finds new information using machine learning and data mining. Machine learning and data mining are irreplaceable subjects and tools for the theory of pattern recognition and in applications of pattern recognition such as bioinformatics and data retrieval. This was the fourth edition of MLDM in Pattern Recognition which is the main event of Technical Committee 17 of the International Association for Pattern Recognition; it started out as a workshop and continued as a conference in 2003. Today, there are many international meetings which are titled “machine learning” and “data mining”, whose topics are text mining, knowledge discovery, and applications. This meeting from the first focused on aspects of machine learning and data mining in pattern recognition problems. We planned to reorganize classical and well-established pattern recognition paradigms from the viewpoints of machine learning and data mining. Though it was a challenging program in the late 1990s, the idea has inspired new starting points in pattern recognition and effects in other areas such as cognitive computer vision.

Keywords

classification computer vision data mining learning machine learning pattern mining

Editors and affiliations

  • Petra Perner
    • 1
  • Atsushi Imiya
    • 2
  1. 1.Institute of Computer Vision and applied Computer SciencesIBaIGermany
  2. 2.Institute of Media and Information TechnologyChiba UniversityJapan

Bibliographic information

  • DOI https://doi.org/10.1007/b138149
  • Copyright Information Springer-Verlag Berlin Heidelberg 2005
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-26923-6
  • Online ISBN 978-3-540-31891-0
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book