Machine Learning and Data Mining in Pattern Recognition

13th International Conference, MLDM 2017, New York, NY, USA, July 15-20, 2017, Proceedings

  • Petra Perner
Conference proceedings MLDM 2017
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10358)

Table of contents

  1. Front Matter
    Pages I-IX
  2. Julio Borges, Martin A. Neumann, Christian Bauer, Yong Ding, Till Riedel, Michael Beigl
    Pages 17-31
  3. Sougata Chaudhuri, Georgios Theocharous, Mohammad Ghavamzadeh
    Pages 32-46
  4. Louis Chartrand, Jackie C. K. Cheung, Mohamed Bouguessa
    Pages 78-90
  5. Christophe Dupuy, Francis Bach, Christophe Diot
    Pages 91-106
  6. Anit Bhandari, Kiran Rama, Nandini Seth, Nishant Niranjan, Parag Chitalia, Stig Berg
    Pages 107-116
  7. Richard Neuberg, Yixin Shi
    Pages 117-131
  8. Youngha Hwang, Saul B. Gelfand
    Pages 163-175
  9. Carolina Medeiros Carvalho, Flávio Luiz Seixas, Aura Conci, Débora Christina Muchaluat-Saade, Jerson Laks
    Pages 176-191
  10. Rekar O. Mohammed, Gavin C. Cawley
    Pages 192-205
  11. Ahmad P. Tafti, Eric LaRose, Jonathan C. Badger, Ross Kleiman, Peggy Peissig
    Pages 206-219
  12. Nathaniel Grabaskas, Dong Si
    Pages 220-232

About these proceedings

Introduction

This book constitutes the refereed proceedings of the 13th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2017, held in New York, NY, USA in July/August 2017.
The 31 full papers presented in this book were carefully reviewed and selected from 150 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multi-media data types such as image mining, text mining, video mining, and Web mining.

Keywords

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.Institute of Computer Vision and Applied Computer SciencesLeipzigGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-62416-7
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-62415-0
  • Online ISBN 978-3-319-62416-7
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349