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Machine Learning and Data Mining in Pattern Recognition

Third International Conference, MLDM 2003, Leipzig, Germany, July 5-7, 2003, proceedings

  • Conference proceedings
  • © 2003

Overview

Editors:
  1. Petra Perner

    1. Institute of Computer Vision and Applied Computer Sciences, Leipzig, Germany

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    You can also search for this editor in PubMed   Google Scholar

  2. Azriel Rosenfeld

    1. Center for Automation Research, University of Maryland, College Park, USA

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 2734)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: MLDM 2003.

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Table of contents (37 papers)

  1. Classification, Retrieval, and Feature Learning

    1. Coevolutionary Feature Learning for Object Recognition

      • Krzysztof Krawiec, Bir Bhanu
      Pages 224-238

  2. Discovery of Frequently or Sequential Patterns

    1. Generalization of Pattern-Growth Methods for Sequential Pattern Mining with Gap Constraints

      • Cláudia Antunes, Arlindo L. Oliveira
      Pages 239-251

    2. Discover Motifs in Multi-dimensional Time-Series Using the Principal Component Analysis and the MDL Principle

      • Yoshiki Tanaka, Kuniaki Uehara
      Pages 252-265

    3. Optimizing Financial Portfolios from the Perspective of Mining Temporal Structures of Stock Returns

      • Kai-Chun Chiu, Lei Xu
      Pages 266-275

    4. Visualizing Sequences of Texts Using Collocational Networks

      • Camilla Magnusson, Hannu Vanharanta
      Pages 276-283

    5. Complexity Analysis of Depth First and FP-Growth Implementations of APRIORI

      • Walter A. Kosters, Wim Pijls, Viara Popova
      Pages 284-292

  3. Bayesian Models and Methods

    1. GO-SPADE: Mining Sequential Patterns over Datasets with Consecutive Repetitions

      • Marion Leleu, Christophe Rigotti, Jean-François Boulicaut, Guillaume Euvrard
      Pages 293-306

    2. Using Test Plans for Bayesian Modeling

      • Rainer Deventer, Joachim Denzler, Heinrich Niemann, Oliver Kreis
      Pages 307-316

    3. Using Bayesian Networks to Analyze Medical Data

      • In-Cheol Kim, Yong-Gyu Jung
      Pages 317-327

    4. A Belief Networks-Based Generative Model for Structured Documents. An Application to the XML Categorization

      • Ludovic Denoyer, Patrick Gallinari
      Pages 328-342

    5. Neural Self-Organization Using Graphs

      • Arpad Barsi
      Pages 343-352

  4. Association Rules Mining

    1. Integrating Fuzziness with OLAP Association Rules Mining

      • Mehmet Kaya, Reda Alhajj
      Pages 353-368

    2. Discovering Association Patterns Based on Mutual Information

      • Bon K. Sy
      Pages 369-378

  5. Applications

    1. Connectionist Probability Estimators in HMM Arabic Speech Recognition Using Fuzzy Logic

      • Lilia Lazli, Mokhtar Sellami
      Pages 379-388

    2. Shape Recovery from an Unorganized Image Sequence

      • Kazuhiko Kawamoto, Atsushi Imiya, Kaoru Hirota
      Pages 389-399

    3. A Learning Autonomous Driver System on the Basis of Image Classification and Evolutional Learning

      • Klaus-Dieter Kuhnert, Michael Krödel
      Pages 400-412

    4. Detecting the Boundary Curve of Planar Random Point Set

      • A. Imiya, K. Tatara, H. Ootani, V. Hlaváč
      Pages 413-424

    5. A Machine Learning Model for Information Retrieval with Structured Documents

      • Benjamin Piwowarski, Patrick Gallinari
      Pages 425-438

  6. Back Matter

    Pages 439-440
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  1. Machine Learning and Data Mining in Pattern Recognition

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About this book

TheInternationalConferenceonMachineLearningandDataMining(MLDM)is the third meeting in a series of biennial events, which started in 1999, organized by the Institute of Computer Vision and Applied Computer Sciences (IBaI) in Leipzig. MLDM began as a workshop and is now a conference, and has brought the topic of machine learning and data mining to the attention of the research community. Seventy-?ve papers were submitted to the conference this year. The program committeeworkedhardtoselectthemostprogressiveresearchinafairandc- petent review process which led to the acceptance of 33 papers for presentation at the conference. The 33 papers in these proceedings cover a wide variety of topics related to machine learning and data mining. The two invited talks deal with learning in case-based reasoning and with mining for structural data. The contributed papers can be grouped into nine areas: support vector machines; pattern dis- very; decision trees; clustering; classi?cation and retrieval; case-based reasoning; Bayesian models and methods; association rules; and applications. We would like to express our appreciation to the reviewers for their precise andhighlyprofessionalwork.WearegratefultotheGermanScienceFoundation for its support of the Eastern European researchers. We appreciate the help and understanding of the editorial sta? at Springer Verlag, and in particular Alfred Hofmann,whosupportedthepublicationoftheseproceedingsintheLNAIseries. Last, but not least, we wish to thank all the speakers and participants who contributed to the success of the conference.

Editors and Affiliations

  • Institute of Computer Vision and Applied Computer Sciences, Leipzig, Germany

    Petra Perner

  • Center for Automation Research, University of Maryland, College Park, USA

    Azriel Rosenfeld

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