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Machine Learning and Data Mining in Pattern Recognition: 10th International Conference, MLDM 2014, St. Petersburg, Russia, July 21-24, 2014, Proceedings de Petra Perner

Machine Learning and Data Mining in Pattern Recognition: 10th International Conference, MLDM 2014, St. Petersburg, Russia, July 21-24, 2014, Proceedings: Lecture Notes in Computer Science, cartea 8556

Editat de Petra Perner
en Limba Engleză Paperback – 4 aug 2014
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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Specificații

ISBN-13: 9783319089782
ISBN-10: 3319089781
Pagini: 552
Ilustrații: XIV, 536 p. 188 illus.
Dimensiuni: 155 x 235 x 32 mm
Greutate: 0.76 kg
Ediția:2014
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence

Locul publicării:Cham, Switzerland

Public țintă

Research

Cuprins

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.

Caracteristici

Up-to-date results Fast track conference proceedings State-of-the-art report Includes supplementary material: sn.pub/extras

Textul de pe ultima copertă

This book constitutes the refereed proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2009, held in Leipzig, Germany, in July 2009.
The 63 revised full papers presented were carefully reviewed and selected from 205 submissions. The papers are organized in topical sections on attribute discretization and data preparation; classification; ensemble classifier learning; associate rules and pattern minig; support vector machines; clustering; novelty and outlier detection; learning; data mining and multimedia data; text mining; aspects of data mining; as well as data mining in medicine.