Machine learning and data mining in pattern recognition : 9th International Conference, MLDM 2013, New York, NY, USA, July 19-25, 2013. Proceedings / Petra Perner (ed.).Material type: TextSeries: Serienbezeichnung | Lecture notes in computer science. Lecture notes in artificial intelligence ; ; 7988. | LNCS sublibrary. SL 7, Artificial intelligence.Publisher: Heidelberg : Springer, Description: 1 online resource (xii, 660 pages) : illustrations (black and white)Content type: text Media type: computer Carrier type: online resourceISBN: 9783642397127; 3642397123Other title: MLDM 2013Subject(s): Pattern perception -- Congresses | Machine learning -- Congresses | Data mining -- Congresses | Computer vision -- Congresses | Data Mining | Artificial Intelligence | Pattern Recognition, Automated | Computer vision | Data mining | Machine learning | Pattern perception | Computer science | Computer software | Data mining | Artificial intelligence | Optical pattern recognition | Pattern Recognition | Algorithm Analysis and Problem ComplexityGenre/Form: Electronic books. | Congress. | Electronic books. | Ebook. | Conference papers and proceedings. Additional physical formats: Print version:: Machine learning and data mining in pattern recognition. MLDM (Conference) (9th : 2013 : New York, N.Y.).DDC classification: 006.4 LOC classification: Q327 | .M53 2013NLM classification: Q 327Online resources: Click here to access online
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International conference proceedings.
Includes author index.
Online resource; title from PDF title page (SpringerLink, viewed July 23, 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.
Print version record.
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.