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Machine Learning and Data Mining in Pattern Recognition: 9th International Conference, MLDM 2013, New York, NY, USA, July 19-25, 2013, Proceedings de Petra Perner

Machine Learning and Data Mining in Pattern Recognition: 9th International Conference, MLDM 2013, New York, NY, USA, July 19-25, 2013, Proceedings: Lecture Notes in Computer Science, cartea 7988

Editat de Petra Perner
en Limba Engleză Paperback – 9 iul 2013
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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Specificații

ISBN-13: 9783642397110
ISBN-10: 3642397115
Pagini: 672
Ilustrații: XII, 660 p. 199 illus.
Dimensiuni: 155 x 235 x 35 mm
Greutate: 0.93 kg
Ediția:2013
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

Theoretical topics for classification.- Clustering.- Association rule and pattern mining.- Specific data mining methods for the different multimedia data types.- Image mining.- Text mining.- Video mining.-Web mining.

Caracteristici

Proceedings of the 9th International Conference on Machine Learning and Data Mining in Pattern Recognition

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.