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Machine Learning and Data Mining in Pattern Recognition

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

  • Conference proceedings
  • © 2005

Overview

Editors:
  1. Petra Perner

    1. Institute of Computer Vision and applied Computer Sciences, IBaI, Germany

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  2. Atsushi Imiya

    1. Institute of Media and Information Technology, Chiba University, Japan

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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 3587)

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

Included in the following conference series:

Conference proceedings info: MLDM 2005.

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Table of contents (68 papers)

  1. Applications of Clustering

    1. Clustering Document Images Using Graph Summaries

      • Eugen Barbu, Pierre Héroux, Sébastien Adam, Eric Trupin
      Pages 194-202

  2. Feature Grouping, Discretization, Selection and Transformation

    1. Feature Selection Method Using Preferences Aggregation

      • Gaëlle Legrand, Nicolas Nicoloyannis
      Pages 203-217

    2. Ranked Modelling with Feature Selection Based on the CPL Criterion Functions

      • Leon Bobrowski
      Pages 218-227

    3. A Grouping Method for Categorical Attributes Having Very Large Number of Values

      • Marc Boullé
      Pages 228-242

    4. Unsupervised Learning of Visual Feature Hierarchies

      • Fabien Scalzo, Justus Piater
      Pages 243-252

    5. Multivariate Discretization by Recursive Supervised Bipartition of Graph

      • Sylvain Ferrandiz, Marc Boullé
      Pages 253-264

    6. CorePhrase: Keyphrase Extraction for Document Clustering

      • Khaled M. Hammouda, Diego N. Matute, Mohamed S. Kamel
      Pages 265-274

    7. A New Multidimensional Feature Transformation for Linear Classifiers and Its Applications

      • EunSang Bak
      Pages 275-284

  3. Applications in Medicine

    1. Comparison of FLDA, MLP and SVM in Diagnosis of Lung Nodule

      • Aristófanes Corrêa Silva, Anselmo Cardoso de Paiva, Alexandre Cesar Muniz de Oliveira
      Pages 285-294

    2. Diagnosis of Lung Nodule Using Reinforcement Learning and Geometric Measures

      • Aristófanes Correâ Silva, Valdeci Ribeiro da Silva Jr., Areolino de Almeida Neto, Anselmo Cardoso de Paiva
      Pages 295-304

    3. Iris Recognition Algorithm Based on Point Covering of High-Dimensional Space and Neural Network

      • Wenming Cao, Jianhui Hu, Gang Xiao, Shoujue Wang
      Pages 305-313

    4. Automatic Clinical Image Segmentation Using Pathological Modelling, PCA and SVM

      • Shuo Li, Thomas Fevens, Adam Krzyżak, Song Li
      Pages 314-324

    5. Improved MRI Mining by Integrating Support Vector Machine Priors in the Bayesian Restoration

      • D. A. Karras, B. G. Mertzios, D. Graveron-Demilly, D. van Ormondt
      Pages 325-333

    6. Prediction of Secondary Protein Structure Content from Primary Sequence Alone – A Feature Selection Based Approach

      • Lukasz Kurgan, Leila Homaeian
      Pages 334-345

    7. Alternative Clustering by Utilizing Multi-objective Genetic Algorithm with Linked-List Based Chromosome Encoding

      • Jun Du, Emin Erkan Korkmaz, Reda Alhajj, Ken Barker
      Pages 346-355

  4. Time Series and Sequential Pattern Mining

    1. Embedding Time Series Data for Classification

      • Akira Hayashi, Yuko Mizuhara, Nobuo Suematsu
      Pages 356-365

    2. Analysis of Time Series of Graphs: Prediction of Node Presence by Means of Decision Tree Learning

      • Horst Bunke, Peter Dickinson, Christophe Irniger, Miro Kraetzl
      Pages 366-375

    3. Disjunctive Sequential Patterns on Single Data Sequence and Its Anti-monotonicity

      • Kazuhiro Shimizu, Takao Miura
      Pages 376-383

    4. Mining Expressive Temporal Associations from Complex Data

      • Keith A. Pray, Carolina Ruiz
      Pages 384-394

    5. Statistical Supports for Frequent Itemsets on Data Streams

      • Pierre-Alain Laur, Jean-Emile Symphor, Richard Nock, Pascal Poncelet
      Pages 395-404
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  1. Machine Learning and Data Mining in Pattern Recognition

Keywords

About this book

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.

Editors and Affiliations

  • Institute of Computer Vision and applied Computer Sciences, IBaI, Germany

    Petra Perner

  • Institute of Media and Information Technology, Chiba University, Japan

    Atsushi Imiya

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