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Machine Learning and Data Mining in Pattern Recognition: 11th International Conference, MLDM 2015, Hamburg, Germany, July 20-21, 2015, Proceedings de Petra Perner

Machine Learning and Data Mining in Pattern Recognition: 11th International Conference, MLDM 2015, Hamburg, Germany, July 20-21, 2015, Proceedings: Lecture Notes in Computer Science, cartea 9166

Editat de Petra Perner
en Limba Engleză Paperback – 13 iul 2015
This book constitutes the refereed proceedings of the 11th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2015, held in Hamburg, Germany in July 2015. The 41 full papers presented were carefully reviewed and selected from 123 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: 9783319210230
ISBN-10: 3319210238
Pagini: 422
Ilustrații: IX, 454 p. 132 illus.
Dimensiuni: 155 x 235 x 30 mm
Greutate: 0.65 kg
Ediția:2015
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

Graph Mining.- Classification and regression.- Sentiment analysis.- Data preparation and missing values.- Association and sequential rule mining.- Support vector machines.- Frequent item set mining and time series analysis.- Clustering.- Text mining.- Applications data mining.

Textul de pe ultima copertă

This book constitutes the refereed proceedings of the 11th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2015, held in Hamburg, Germany, in July 2015.
The 41 full papers presented were carefully reviewed and selected from 123 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.

Caracteristici

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