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Machine Learning and Data Mining in Pattern Recognition: 12th International Conference, MLDM 2016, New York, NY, USA, July 16-21, 2016, Proceedings de Petra Perner

Machine Learning and Data Mining in Pattern Recognition: 12th International Conference, MLDM 2016, New York, NY, USA, July 16-21, 2016, Proceedings: Lecture Notes in Computer Science, cartea 9729

Editat de Petra Perner
en Limba Engleză Paperback – 28 iun 2016
This book constitutes the refereed proceedings of the 12th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2016, held in New York, NY, USA in July 2016. The 58 regular papers presented in this book were carefully reviewed and selected from 169 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: 9783319419190
ISBN-10: 3319419196
Pagini: 776
Ilustrații: XIII, 807 p. 291 illus.
Dimensiuni: 155 x 235 x 42 mm
Greutate: 1.13 kg
Ediția:1st ed. 2016
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence

Locul publicării:Cham, Switzerland

Cuprins

Classification.- Clustering.- Association rule.- Pattern mining.- Image mining.-Text mining.- Video mining.- Web mining.

Caracteristici

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.