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  • Conference proceedings
  • © 2009

Machine Learning and Data Mining in Pattern Recognition

6th International Conference, MLDM 2009, Leipzig, Germany, July 23-25, 2009, Proceedings

Editors:

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 5632)

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

Conference series link(s): MLDM: International Conference on Machine Learning and Data Mining in Pattern Recognition

Conference proceedings info: MLDM 2009.

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Table of contents (61 papers)

  1. Front Matter

  2. Attribute Discretization and Data Preparation

    1. Selection of Subsets of Ordered Features in Machine Learning

      • O. Seredin, A. Kopylov, V. Mottl
      Pages 16-28
    2. Combination of Vector Quantization and Visualization

      • Olga Kurasova, Alma Molytė
      Pages 29-43
    3. Discretization of Target Attributes for Subgroup Discovery

      • Katherine Moreland, Klaus Truemper
      Pages 44-52
    4. Using Resampling Techniques for Better Quality Discretization

      • Taimur Qureshi, Djamel A. Zighed
      Pages 68-81
  3. Classification

    1. A Large Margin Classifier with Additional Features

      • Xinwang Liu, Jianping Yin, En Zhu, Guomin Zhang, Yubin Zhan, Miaomiao Li
      Pages 82-95
    2. Sequential EM for Unsupervised Adaptive Gaussian Mixture Model Based Classifier

      • Bashar Awwad Shiekh Hasan, John Q. Gan
      Pages 96-106
    3. Optimal Double-Kernel Combination for Classification

      • Feng Wang, Hongbin Zhang
      Pages 107-122
    4. Efficient AdaBoost Region Classification

      • M. Moed, E. N. Smirnov
      Pages 123-136
    5. PMCRI: A Parallel Modular Classification Rule Induction Framework

      • Frederic Stahl, Max Bramer, Mo Adda
      Pages 148-162
    6. Dynamic Score Combination: A Supervised and Unsupervised Score Combination Method

      • Roberto Tronci, Giorgio Giacinto, Fabio Roli
      Pages 163-177
    7. ODDboost: Incorporating Posterior Estimates into AdaBoost

      • Olga Barinova, Dmitry Vetrov
      Pages 178-190
  4. Ensemble Classifier Learning

    1. Ensemble Learning: A Study on Different Variants of the Dynamic Selection Approach

      • João Mendes-Moreira, Alipio Mario Jorge, Carlos Soares, Jorge Freire de Sousa
      Pages 191-205
    2. Relevance and Redundancy Analysis for Ensemble Classifiers

      • Rakkrit Duangsoithong, Terry Windeatt
      Pages 206-220
    3. Drift-Aware Ensemble Regression

      • Frank Rosenthal, Peter Benjamin Volk, Martin Hahmann, Dirk Habich, Wolfgang Lehner
      Pages 221-235
    4. Concept Drifting Detection on Noisy Streaming Data in Random Ensemble Decision Trees

      • Peipei Li, Xuegang Hu, Qianhui Liang, Yunjun Gao
      Pages 236-250
  5. Association Rules and Pattern Mining

Other Volumes

  1. Machine Learning and Data Mining in Pattern Recognition

About this book

There is no royal road to science, and only those who do not dread the fatiguing climb of its steep paths have a chance of gaining its luminous summits. Karl Marx A Universial Genius of the 19th Century Many scientists from all over the world during the past two years since the MLDM 2007 have come along on the stony way to the sunny summit of science and have worked hard on new ideas and applications in the area of data mining in pattern r- ognition. Our thanks go to all those who took part in this year's MLDM. We appre- ate their submissions and the ideas shared with the Program Committee. We received over 205 submissions from all over the world to the International Conference on - chine Learning and Data Mining, MLDM 2009. The Program Committee carefully selected the best papers for this year’s program and gave detailed comments on each submitted paper. There were 63 papers selected for oral presentation and 17 papers for poster presentation. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data-mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining. Among these topics this year were special contributions to subtopics such as attribute discre- zation and data preparation, novelty and outlier detection, and distances and simila- ties.

Keywords

  • Cluster
  • Clustering
  • Support Vector Machine
  • classification
  • data mining
  • machine learning
  • multimedia
  • pattern recognition
  • text mining

Editors and Affiliations

  • Institut für Bildverarbeitung und angewandte Informatik, Leipzig, Germany

    Petra Perner

Bibliographic Information

Buying options

eBook USD 84.99
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 109.99
Price excludes VAT (Canada)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions