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Machine Learning and Data Mining in Pattern Recognition [electronic resource] : 10th International Conference, MLDM 2014, St. Petersburg, Russia, July 21-24, 2014. Proceedings / edited by Petra Perner.

By: Contributor(s): Material type: TextTextSeries: Lecture Notes in Computer Science ; 8556Publisher: Cham : Springer International Publishing : Imprint: Springer, 2014Description: XIV, 536 p. 188 illus. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783319089799
Subject(s): Additional physical formats: Printed edition:: No titleDDC classification:
  • 006.3 23
LOC classification:
  • Q334-342
  • TJ210.2-211.495
Online resources:
Contents:
Theoretical topics for classification -- Clustering -- Association rule.-  Pattern mining.- Specific data mining methods for the different multimedia data types.- Image mining -- Text mining -- Video mining.-  Web mining.
In: Springer eBooksSummary: This book constitutes the refereed proceedings of the 10th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2014, held in St. Petersburg, Russia in July 2014. The 40 full papers presented were carefully reviewed and selected from 128 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.
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Theoretical topics for classification -- Clustering -- Association rule.-  Pattern mining.- Specific data mining methods for the different multimedia data types.- Image mining -- Text mining -- Video mining.-  Web mining.

This book constitutes the refereed proceedings of the 10th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2014, held in St. Petersburg, Russia in July 2014. The 40 full papers presented were carefully reviewed and selected from 128 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.

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