Machine Learning and Data Mining in Pattern Recognition

5th International Conference, MLDM 2007, Leipzig, Germany, July 18-20, 2007. Proceedings

  • Editors
  • Petra Perner

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4571)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 4571)

Table of contents

  1. Front Matter
  2. Invited Talk

  3. Classification

  4. Feature Selection, Extraction and Dimensionality Reduction

    1. Ireneusz Czarnowski, Piotr Jȩdrzejowicz
      Pages 117-130
    2. Moonhwi Lee, Cheong Hee Park
      Pages 131-143
    3. Haibin Cheng, Haifeng Chen, Guofei Jiang, Kenji Yoshihira
      Pages 144-159
  5. Clustering

    1. Sandro Saitta, Benny Raphael, Ian F. C. Smith
      Pages 174-187
    2. Xuegang Hu, Dongbo Wang, Xindong Wu
      Pages 188-202
    3. Ivan O Kyrgyzov, Olexiy O Kyrgyzov, Henri Maître, Marine Campedel
      Pages 203-217
    4. Tomoya Sakai, Atsushi Imiya, Takuto Komazaki, Shiomu Hama
      Pages 218-232
    5. Airel Pérez Suárez, José E. Medina Pagola
      Pages 248-262

About these proceedings

Introduction

MLDM / ICDM Medaillie Meissner Porcellan, the “White Gold” of King August the Strongest of Saxonia Gottfried Wilhelm von Leibniz, the great mathematician and son of Leipzig, was watching over us during our event in Machine Learning and Data Mining in Pattern Recognition (MLDM 2007). He can be proud of what we have achieved in this area so far. We had a great research program this year. This was the fifth MLDM in Pattern Recognition event held in Leipzig (www.mldm.de). Today, there are many international meetings carrying the title machine learning and data mining, whose topics are text mining, knowledge discovery, and applications. This meeting from the very first event has 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 view points of machine learning and data mining. Although it was a challenging program in the late 1990s, the idea has provided new starting points in pattern recognition and has influenced other areas such as cognitive computer vision. For this edition, the Program Committee received 258 submissions from 37 countries (see Fig. 1). To handle this high number of papers was a big challenge for the reviewers. Every paper was thoroughly reviewed and all authors received a detailed report on their submitted work.

Keywords

Spam classification cognition data mining learning machine learning pattern recognition

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-73499-4
  • Copyright Information Springer-Verlag Berlin Heidelberg 2007
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-73498-7
  • Online ISBN 978-3-540-73499-4
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book