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Weitersagen:


Herausgeber: 
  • René Doursat
  • Nicolas Bredeche
  • Taras Kowaliw
  • Growing Adaptive Machines: Combining Development and Learning in Artificial Neural Networks 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 5-10 Tagen versandfertig
    Veröffentlichung:  September 2016  
    Genre:  Naturwissensch., Medizin, Technik 
    ISBN:  9783662509449 
    EAN-Code: 
    9783662509449 
    Verlag:  Springer Berlin Heidelberg 
    Einband:  Kartoniert  
    Sprache:  English  
    Serie:  #557 - Studies in Computational Intelligence  
    Dimensionen:  H 235 mm / B 155 mm / D 15 mm 
    Gewicht:  417 gr 
    Seiten:  272 
    Zus. Info:  Paperback 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    The pursuit of artificial intelligence has been a highly active domain of research for decades, yielding exciting scientific insights and productive new technologies. In terms of generating intelligence, however, this pursuit has yielded only limited success. This book explores the hypothesis that adaptive growth is a means of moving forward. By emulating the biological process of development, we can incorporate desirable characteristics of natural neural systems into engineered designs, and thus move closer towards the creation of brain-like systems. The particular focus is on how to design artificial neural networks for engineering tasks.

    The book consists of contributions from 18 researchers, ranging from detailed reviews of recent domains by senior scientists, to exciting new contributions representing the state of the art in machine learning research. The book begins with broad overviews of artificial neurogenesis and bio-inspired machine learning, suitable both as an introduction to the domains and as a reference for experts. Several contributions provide perspectives and future hypotheses on recent highly successful trains of research, including deep learning, the HyperNEAT model of developmental neural network design, and a simulation of the visual cortex. Other contributions cover recent advances in the design of bio-inspired artificial neural networks, including the creation of machines for classification, the behavioural control of virtual agents, the desi

    gn of virtual multi-component robots and morphologies, and the creation of flexible intelligence. Throughout, the contributors share their vast expertise on the means and benefits of creating brain-like machines.

    This book is appropriate for advanced students and practitioners of artificial intelligence and machine learning.

      



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