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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 / edited by Petra Perner.

Por: Colaborador(es): Tipo de material: TextoTextoSeries Lecture Notes in Computer Science ; 7988Editor: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Descripción: xii, 660 páginas 199 ilustraciones recurso en líneaTipo de contenido:
  • texto
Tipo de medio:
  • computadora
Tipo de portador:
  • recurso en línea
ISBN:
  • 9783642397127
Formatos físicos adicionales: Edición impresa:: Sin títuloClasificación LoC:
  • Q334-342
Recursos en línea:
Contenidos:
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.
Resumen: 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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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.

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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