1 option
Machine Learning and Data Mining in Pattern Recognition : 10th International Conference, MLDM 2014, St. Petersburg, Russia, July 21-24, 2014, Proceedings / edited by Petra Perner.
SpringerLink Books Lecture Notes In Computer Science (LNCS) (1997-2024) Available online
View online- Format:
- Book
- Series:
- Computer Science (Springer-11645)
- Lecture notes in computer science. Lecture notes in artificial intelligence ; 8556.
- Lecture Notes in Artificial Intelligence ; 8556
- Language:
- English
- Subjects (All):
- Artificial intelligence.
- Data mining.
- Pattern perception.
- Algorithms.
- Application software.
- Database management.
- Artificial Intelligence.
- Data Mining and Knowledge Discovery.
- Pattern Recognition.
- Algorithm Analysis and Problem Complexity.
- Information Systems Applications (incl. Internet).
- Database Management.
- Local Subjects:
- Artificial Intelligence.
- Data Mining and Knowledge Discovery.
- Pattern Recognition.
- Algorithm Analysis and Problem Complexity.
- Information Systems Applications (incl. Internet).
- Database Management.
- Physical Description:
- 1 online resource (XIV, 536 pages) : 188 illustrations.
- Edition:
- First edition 2014.
- Contained In:
- Springer eBooks
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2014.
- System Details:
- text file PDF
- Summary:
- 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.
- 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.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-319-08979-9
- 9783319089799
- Access Restriction:
- Restricted for use by site license.
The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.