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Machine Learning and Data Mining in Pattern Recognition

Second International Workshop, MLDM 2001, Leipzig, Germany, July 25-27, 2001. Proceedings

  • Conference proceedings
  • © 2001

Overview

Editors:
  1. Petra Perner

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

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 2123)

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

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Table of contents (28 papers)

  1. Front Matter

    Pages I-XI
    Download chapter PDF

  2. Invited Paper

    1. Technology of Text Mining

      • Ari Visa
      Pages 1-11

    2. Evaluation of Clinical Relevance of Clinical Laboratory Investigations by Data Mining

      • Ulrich Sack, Manja Kamprad
      Pages 12-22

  3. Case-Based Reasoning and Associative Memory

    1. Temporal Abstractions and Case-Based Reasoning for Medical Course Data: Two Prognostic Applications

      • Rainer Schmidt, Lothar Gierl
      Pages 23-34

    2. Are Case-Based Reasoning and Dissimilarity-Based Classification Two Sides of the Same Coin?

      • Petra Perner
      Pages 35-51

    3. FAM-Based Fuzzy Inference for Detecting Shot Transitions

      • Seok-Woo Jang, Gye-Young Kim, Hyung-Il Choi
      Pages 52-61

  4. Rule Induction and Grammars

    1. Rule-Based Ensemble Solutions for Regression

      • Nitin Indurkhya, Sholom M. Weiss
      Pages 62-72

    2. Learning XML Grammars

      • Henning Fernau
      Pages 73-87

    3. First-Order Rule Induction for the Recognition of Morphological Patterns in Topographic Maps

      • D. Malerba, F. Esposito, A. Lanza, F. A. Lisi
      Pages 88-101

  5. Clustering and Conceptual Clustering

    1. Concepts Learning with Fuzzy Clustering and Relevance Feedback

      • Bir Bhanu, Anlei Dong
      Pages 102-116

    2. LC: A Conceptual Clustering Algorithm

      • José Fco. Martinez-Trinidad, Guillermo Sánchez-Diaz
      Pages 117-127

  6. Data Mining on Signal, Images, Text and Temporal-Spatial Data

    1. Data Mining Approach Based on Information-Statistical Analysis: Application to Temporal-Spatial Data

      • Bon K. Sy, Arjun K. Gupta
      Pages 128-140

    2. A Hybrid Tool for Data Mining in Picture Archiving System

      • Petra Perner, Tatjana Belikova
      Pages 141-156

    3. Feature Selection for a Real-World Learning Task

      • D. Kollmar, D.H. Hellmann
      Pages 157-172

    4. Automatic Identification of Diatoms Using Decision Forests

      • Stefan Fischer, Horst Bunke
      Pages 173-183

    5. Validation of Text Clustering Based on Document Contents

      • Jarmo Toivonen, Ari Visa, Tomi Vesanen, Barbro Back, Hannu Vanharanta
      Pages 184-195

  7. Nonlinear Function Learning and Neural Net Based Learning

    1. Statistical and Neural Approaches for Estimating Parameters of a Speckle Model Based on the Nakagami Distribution

      • Mark P. Wachowiak, Renata Smolíková, Mariofanna G. Milanova, Adel S. Elmaghraby
      Pages 196-205

    2. How to Automate Neural Net Based Learning

      • Roland Linder, Siegfried J. Pöppl
      Pages 206-216

    3. Nonlinear Function Learning and Classification Using Optimal Radial Basis Function Networks

      • Adam Krzyżak
      Pages 217-225

  8. Learning for Handwriting Recognition

    1. Local Learning Framework for Recognition of Lowercase Handwritten Characters

      • Jian-xiong Dong, Adam Krzyżak, C. Y. Suen
      Pages 226-238
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Keywords

About this book

This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition, MLDM 2001, held in Leipzig, Germany in July 2001.
The 26 revised full papers presented together with two invited papers were carefully reviewed and selected for inclusion in the proceedings. The papers are organized in topical sections on case-based reasoning and associative memory; rule induction and grammars; clustering and conceptual clustering; data mining on signals, images, and spatio-temporal data; nonlinear function learning and neural net based learning; learning for handwriting recognition; statistical and evolutionary learning; and content-based image retrieval.

Editors and Affiliations

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

    Petra Perner

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