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Link to original content: https://doi.org/10.1007/978-3-642-39712-7
Machine Learning and Data Mining in Pattern Recognition: 9th International Conference, MLDM 2013, New York, NY, USA, July 19-25, 2013, Proceedings | SpringerLink
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Machine Learning and Data Mining in Pattern Recognition

9th International Conference, MLDM 2013, New York, NY, USA, July 19-25, 2013, Proceedings

  • Conference proceedings
  • © 2013

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Editors:
  1. Petra Perner

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

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  • Proceedings of the 9th International Conference on Machine Learning and Data Mining in Pattern Recognition

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 7988)

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

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Conference proceedings info: MLDM 2013.

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About this book

This book constitutes the refereed proceedings of the 9th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2013, held in New York, USA in July 2013. The 51 revised full papers presented were carefully reviewed and selected from 212 submissions. The papers cover the topics ranging 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.

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Table of contents (49 papers)

  1. Front Matter

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  2. The Gapped Spectrum Kernel for Support Vector Machines

    • Taku Onodera, Tetsuo Shibuya
    Pages 1-15

  3. Typhoon Damage Scale Forecasting with Self-Organizing Maps Trained by Selective Presentation Learning

    • Kazuhiro Kohara, Isao Sugiyama
    Pages 16-26

  4. Dynamic-Radius Species-Conserving Genetic Algorithm for the Financial Forecasting of Dow Jones Index Stocks

    • Michael Scott Brown, Michael J. Pelosi, Henry Dirska
    Pages 27-41

  5. Multi Model Transfer Learning with RULES Family

    • Hebah ElGibreen, Mehmet Sabih Aksoy
    Pages 42-56

  6. 3D Geovisualisation Techniques Applied in Spatial Data Mining

    • Carlos Roberto Valêncio, Thatiane Kawabata, Camila Alves de Medeiros, Rogéria Cristiane Gratão de Souza, José Márcio Machado
    Pages 57-68

  7. Improving the Efficiency of Distributed Data Mining Using an Adjustment Work Flow

    • Jie Gao, Jörg Denzinger
    Pages 69-83

  8. Sign Language Recognition with Support Vector Machines and Hidden Conditional Random Fields: Going from Fingerspelling to Natural Articulated Words

    • César Roberto de Souza, Ednaldo Brigante Pizzolato
    Pages 84-98

  9. Classification and Outlier Detection Based on Topic Based Pattern Synthesis

    • Samrat Kokkula, Narasimha Murty Musti
    Pages 99-114

  10. Decremental Learning of Evolving Fuzzy Inference Systems: Application to Handwritten Gesture Recognition

    • Manuel Bouillon, Eric Anquetil, Abdullah Almaksour
    Pages 115-129

  11. Unsupervised Tagging of Spanish Lyrics Dataset Using Clustering

    • Fabio Leonardo Parra, Elizabeth León
    Pages 130-143

  12. Feature Learning for Detection and Prediction of Freezing of Gait in Parkinson’s Disease

    • Sinziana Mazilu, Alberto Calatroni, Eran Gazit, Daniel Roggen, Jeffrey M. Hausdorff, Gerhard Tröster
    Pages 144-158

  13. Multi-document Text Summarization Using Topic Model and Fuzzy Logic

    • Sanghoon Lee, Saeid Belkasim, Yanqing Zhang
    Pages 159-168

  14. A Pattern Based Two-Stage Text Classifier

    • Moch Arif Bijaksana, Yuefeng Li, Abdulmohsen Algarni
    Pages 169-182

  15. Applying a Lightweight Iterative Merging Chinese Segmentation in Web Image Annotation

    • Chuen-Min Huang, Yen-Jia Chang
    Pages 183-194

  16. Relevance as a Metric for Evaluating Machine Learning Algorithms

    • Aravind Kota Gopalakrishna, Tanir Ozcelebi, Antonio Liotta, Johan J. Lukkien
    Pages 195-208

  17. Preceding Rule Induction with Instance Reduction Methods

    • Osama Othman, Christopher H. Bryant
    Pages 209-218

  18. Analytic Feature Selection for Support Vector Machines

    • Carly Stambaugh, Hui Yang, Felix Breuer
    Pages 219-233

  19. Evaluation of Hyperspectral Image Classification Using Random Forest and Fukunaga-Koontz Transform

    • Semih Dinç, Ramazan Savas Aygün
    Pages 234-245

  20. SOM++: Integration of Self-Organizing Map and K-Means++ Algorithms

    • Yunus Dogan, Derya Birant, Alp Kut
    Pages 246-259
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Other volumes

  1. Machine Learning and Data Mining in Pattern Recognition

Editors and Affiliations

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

    Petra Perner

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