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Herausgeber: 
  • Wolfgang Stolzmann
  • Stewart W. Wilson
  • Tim Kovacs
  • Pier Luca Lanzi
  • Keiki Takadama
  • Xavier Llorà
  • Learning Classifier Systems: International Workshops, IWLCS 2003-2005, Revised Selected Papers 
     

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


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 5-10 Tagen versandfertig
    Veröffentlichung:  März 2007  
    Genre:  EDV / Informatik 
     
    adaptiveexplorationrate / AlgorithmicLearning / Algorithms / complexity / Constraints / Data Mining / datamining / Decisiontrees
    ISBN:  9783540712305 
    EAN-Code: 
    9783540712305 
    Verlag:  Springer 
    Einband:  Kartoniert  
    Sprache:  English  
    Serie:  Lecture Notes in Artificial Intelligence
    #4399 - Lecture Notes in Computer Science  
    Dimensionen:  H 235 mm / B 155 mm / D 20 mm 
    Gewicht:  552 gr 
    Seiten:  364 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    The work embodied in this volume was presented across three consecutive e- tions of the International Workshop on Learning Classi?er Systems that took place in Chicago (2003), Seattle (2004), and Washington (2005). The Genetic and Evolutionary Computation Conference, the main ACM SIGEvo conference, hosted these three editions. The topics presented in this volume summarize the wide spectrum of interests of the Learning Classi?er Systems (LCS) community. The topics range from theoretical analysis of mechanisms to practical cons- eration for successful application of such techniques to everyday data-mining tasks. When we started editing this volume, we faced the choice of organizing the contents in a purely chronologicalfashion or as a sequence of related topics that help walk the reader across the di?erent areas. In the end we decided to or- nize the contents by area, breaking the time-line a little. This is not a simple endeavor as we can organize the material using multiple criteria. The tax- omy below is our humble e?ort to provide a coherent grouping. Needless to say, some works may fall in more than one category. The four areas are as follows: Knowledge representation. These chapters elaborate on the knowledge r- resentations used in LCS. Knowledge representation is a key issue in any learning system and has implications for what it is possible to learn and what mechanisms shouldbe used. Four chapters analyze di?erent knowledge representations and the LCS methods used to manipulate them.

      



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