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Herausgeber: 
  • Thomas Hofmann
  • Dimitrios Gunopulos
  • Michalis Vazirgiannis
  • Donato Malerba
  • Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2010, Athens, Greece, September 5-9, 2011, Proceedings, Part I 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  September 2011  
    Genre:  EDV / Informatik 
     
    Algorithm Analysis and Problem Complexity / Algorithms / Algorithms & data structures / Artificial Intelligence / C / computer science / Data Warehousing / Database Management
    ISBN:  9783642237799 
    EAN-Code: 
    9783642237799 
    Verlag:  Springer EN 
    Einband:  Kartoniert  
    Sprache:  English  
    Serie:  Lecture Notes in Artificial Intelligence
    #6911 - Lecture Notes in Computer Science  
    Dimensionen:  H 235 mm / B 155 mm / D  
    Gewicht:  1136 gr 
    Seiten:  649 
    Illustration:  XXX, 649 p. 
    Zus. Info:  EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    This three-volume set LNAI 6911, LNAI 6912, and LNAI 6913 constitutes the refereed proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2011, held in Athens, Greece, in September 2011. The 121 revised full papers presented together with 10 invited talks and 11 demos in the three volumes, were carefully reviewed and selected from about 600 paper submissions. The papers address all areas related to machine learning and knowledge discovery in databases as well as other innovative application domains such as supervised and unsupervised learning with some innovative contributions in fundamental issues; dimensionality reduction, distance and similarity learning, model learning and matrix/tensor analysis; graph mining, graphical models, hidden markov models, kernel methods, active and ensemble learning, semi-supervised and transductive learning, mining sparse representations, model learning, inductive logic programming, and statistical learning. a significant part of the papers covers novel and timely applications of data mining and machine learning in industrial domains.
      



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