Advertisement

© 2018

Machine Learning and Data Mining in Pattern Recognition

14th International Conference, MLDM 2018, New York, NY, USA, July 15-19, 2018, Proceedings, Part II

  • Petra Perner
Conference proceedings MLDM 2018

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10935)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 10935)

Table of contents

  1. Front Matter
    Pages I-XV
  2. Nima Shahbazi, Rohollah Soltani, Jarek Gryz
    Pages 16-27
  3. John Velandia, Gustavo Pérez, Holman Bolivar
    Pages 28-41
  4. Erkan Cetiner, Vehbi Cagri Gungor, Taskin Kocak
    Pages 72-86
  5. Semih Dinc, Babak Rahbarinia, Luis Cueva-Parra
    Pages 87-102
  6. Divyanshi Galla, James Burke
    Pages 103-116
  7. Lorenzo Putzu, Luca Piras, Giorgio Giacinto
    Pages 117-131
  8. Bernhard Gahr, Benjamin Ryder, André Dahlinger, Felix Wortmann
    Pages 183-197
  9. Shwet Mani, Sneha Kumari, Ayushi Jain, Prabhat Kumar
    Pages 198-209
  10. Johannes Villmow, Marco Wrzalik, Dirk Krechel
    Pages 210-217
  11. Scott Wahl, John Sheppard
    Pages 231-245
  12. Rafaella Leandra Souza do Nascimento, Ricardo Batista das Neves Junior, Manoel Alves de Almeida Neto, Roberta Andrade de Araújo Fagundes
    Pages 246-257

Other volumes

  1. 14th International Conference, MLDM 2018, New York, NY, USA, July 15-19, 2018, Proceedings, Part I
  2. Machine Learning and Data Mining in Pattern Recognition
    14th International Conference, MLDM 2018, New York, NY, USA, July 15-19, 2018, Proceedings, Part II

About these proceedings

Introduction

This two-volume set LNAI 10934 and LNAI 10935 constitutes the refereed proceedings of the 14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018, held in New York, NY, USA in July 2018. 
The 92 regular papers presented in this two-volume set were carefully reviewed and selected from 298 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

machine learning data mining pattern recognition medical data mining frequent item set mining time series mining classification feature selection clustering image mining graph mining process mining support vector machines artificial intelligence neural networks natural language processing systems internet image segmentation world wide web support vector

Editors and affiliations

  • Petra Perner
    • 1
  1. 1.Institute of Computer Vision and Applied Computer SciencesLeipzigGermany

Bibliographic information

Industry Sectors
Automotive
Chemical Manufacturing
Biotechnology
IT & Software
Telecommunications
Law
Consumer Packaged Goods
Pharma
Materials & Steel
Finance, Business & Banking
Electronics
Energy, Utilities & Environment
Aerospace
Oil, Gas & Geosciences
Engineering