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Machine Learning and Data Mining in Pattern Recognition: 14th International Conference, MLDM 2018, New York, NY, USA, July 15-19, 2018, Proceedings, Part I de Petra Perner

Machine Learning and Data Mining in Pattern Recognition: 14th International Conference, MLDM 2018, New York, NY, USA, July 15-19, 2018, Proceedings, Part I: Lecture Notes in Computer Science, cartea 10934

Editat de Petra Perner
en Limba Engleză Paperback – 8 iul 2018
This two-volume set LNAI 10934 and LNAI 10935 constitutes the refereed proceedings of the 14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018, held in New York, NY, USA in July 2018. 
The 92 regular papers presented in this two-volume set were carefully reviewed and selected from 298 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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Specificații

ISBN-13: 9783319961354
ISBN-10: 3319961357
Pagini: 494
Ilustrații: XV, 455 p. 261 illus., 112 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.65 kg
Ediția:1st ed. 2018
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence

Locul publicării:Cham, Switzerland

Cuprins

Classification.- Clustering.- Image mining.- Text mining.- Video mining.-Web mining.- Graph mining.- Process 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.