"Machine Learning and Data Mining in Pattern Recognition" - Information and Links:

Machine Learning and Data Mining in Pattern Recognition - Info and Reading Options

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

"Machine Learning and Data Mining in Pattern Recognition" was published by Springer in 2013 - Berlin, Heidelberg, it has 660 pages and the language of the book is English.


“Machine Learning and Data Mining in Pattern Recognition” Metadata:

  • Title: ➤  Machine Learning and Data Mining in Pattern Recognition
  • Authors:
  • Language: English
  • Number of Pages: 660
  • Publisher: Springer
  • Publish Date:
  • Publish Location: Berlin, Heidelberg

“Machine Learning and Data Mining in Pattern Recognition” Subjects and Themes:

Edition Specifications:

  • Pagination: xii, 660

Edition Identifiers:

AI-generated Review of “Machine Learning and Data Mining in Pattern Recognition”:


"Machine Learning and Data Mining in Pattern Recognition" Description:

The Open Library:

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.

Open Data:

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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