Syndetics cover image
Image from Syndetics

Machine Learning and Data Mining in Pattern Recognition [electronic resource] : 11th International Conference, MLDM 2015, Hamburg, Germany, July 20-21, 2015, Proceedings / edited by Petra Perner.

Contributor(s): Material type: TextSeries: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2015Edition: 1st ed. 2015Description: IX, 454 p. 132 illus. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783319210247
Subject(s): DDC classification:
  • 006.312 23
Online resources:
Contents:
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.
Summary: 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.
No physical items for this record

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.

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.

Licensed e-book