Machine Learning and Data Mining in Pattern Recognition: 13th International Conference, MLDM 2017, New York, NY, USA, July 15-20, 2017, Proceedings - Info and Reading Options
"Machine Learning and Data Mining in Pattern Recognition: 13th International Conference, MLDM 2017, New York, NY, USA, July 15-20, 2017, Proceedings" was published by Springer International Publishing : Imprint: Springer in 2017 - Cham and it has 1 pages.
“Machine Learning and Data Mining in Pattern Recognition: 13th International Conference, MLDM 2017, New York, NY, USA, July 15-20, 2017, Proceedings” Metadata:
- Title: ➤ Machine Learning and Data Mining in Pattern Recognition: 13th International Conference, MLDM 2017, New York, NY, USA, July 15-20, 2017, Proceedings
- Number of Pages: 1
- Publisher: ➤ Springer International Publishing : Imprint: Springer
- Publish Date: 2017
- Publish Location: Cham
“Machine Learning and Data Mining in Pattern Recognition: 13th International Conference, MLDM 2017, New York, NY, USA, July 15-20, 2017, Proceedings” Subjects and Themes:
- Subjects: ➤ Algorithms - Application software - Artificial intelligence - Data mining - Database management - Pattern recognition - Artificial Intelligence - Algorithm Analysis and Problem Complexity - Data Mining and Knowledge Discovery - Database Management - Information Systems Applications (incl. Internet) - Pattern Recognition
Edition Identifiers:
- ISBN-13: 9783319624167
- All ISBNs: 9783319624167
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"Machine Learning and Data Mining in Pattern Recognition: 13th International Conference, MLDM 2017, New York, NY, USA, July 15-20, 2017, Proceedings" Description:
Open Data:
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
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