Machine Learning and Data Mining in Pattern Recognition

12th International Conference, MLDM 2016, New York, NY, USA, July 16-21, 2016, Proceedings

  • Petra Perner

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9729)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 9729)

Table of contents

  1. Front Matter
    Pages I-XIII
  2. Robert E. Marmelstein, Alexander L. Hunt, Christoper Eroh
    Pages 1-14
  3. Yuan Wang, Xiaochun Wang, Xia Li Wang
    Pages 15-27
  4. Turki Turki, William Bassett, Jason T. L. Wang
    Pages 28-42
  5. Nour El Islem Karabadji, Sabeur Aridhi, Hassina Seridi
    Pages 43-57
  6. Vinh-Trung Luu, Mathis Ripken, Germain Forestier, Frédéric Fondement, Pierre-Alain Muller
    Pages 58-72
  7. Babak Khosravifar, Mohamed Bouguessa
    Pages 73-87
  8. Kiran Rama, Shashank Shekhar, John Kiran, Raghava Rau, Sam Pritchett, Anit Bhandari et al.
    Pages 88-97
  9. Daniel Braun, Michael Singhof, Stefan Conrad
    Pages 98-112
  10. Loai AbdAllah, Ilan Shimshoni
    Pages 113-127
  11. Zeev Volkovich
    Pages 128-142
  12. Ilias Gialampoukidis, Stefanos Vrochidis, Ioannis Kompatsiaris
    Pages 170-184
  13. Philippe Fournier-Viger, Souleymane Zida, Jerry Chun-Wei Lin, Cheng-Wei Wu, Vincent S. Tseng
    Pages 199-213
  14. Vincent Brault, Julien Chiquet, Céline Lévy-Leduc
    Pages 214-228
  15. Yu Zhang, Tse-Chuan Yang, Stephen A. Matthews
    Pages 229-235
  16. Jerry Chun-Wei Lin, Wensheng Gan, Philippe Fournier-Viger, Tzung-Pei Hong
    Pages 236-250

About these proceedings


This book constitutes the refereed proceedings of the 12th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2016, held in New York, NY, USA in July 2016. The 58 regular papers presented in this book were carefully reviewed and selected from 169 submissions. 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.


data mining machine learning natural language processing social network analysis topic modeling anomaly detection association rule bayesian network big data case-based reasoning cellular networks clustering ensemble methods feature extraction fuzzy logic information retrieval sequential pattern mining similarity measures spatial data mining SVM

Editors and affiliations

  • Petra Perner
    • 1
  1. 1.IBaIInst of Comp Vision and applied Comp SciLeipzigGermany

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-41919-0
  • Online ISBN 978-3-319-41920-6
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book