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Herausgeber: 
  • Nikola K. Kasabov
  • Valeri Mladenov
  • Petia Koprinkova-Hristova
  • Artificial Neural Networks: Methods and Applications in Bio-/Neuroinformatics 
     

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


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  September 2014  
    Genre:  Naturwissensch., Medizin, Technik 
     
    Automatic control engineering / bioinformatics / C / Computational and Systems Biology / Computational Biology/Bioinformatics / Computational Intelligence / Control and Systems Theory / Control engineering
    ISBN:  9783319099026 
    EAN-Code: 
    9783319099026 
    Verlag:  Springer EN 
    Einband:  Gebunden  
    Sprache:  English  
    Serie:  #04 - Springer Series in Bio-/Neuroinformatics  
    Dimensionen:  H 235 mm / B 155 mm / D  
    Gewicht:  1133 gr 
    Seiten:  488 
    Illustration:  IX, 488 p. 168 illus., 98 illus. in color., farbige Illustrationen, schwarz-weiss Illustrationen 
    Zus. Info:  EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new algorithms for prototype selection, and group structure discovering. Moreover, the book discusses one-class support vector machines for pattern recognition, handwritten digit recognition, time series forecasting and classification, and anomaly identification in data analytics and automated data analysis. By presenting the state-of-the-art and discussing the current challenges in the fields of artificial neural networks, bioinformatics and neuroinformatics, the book is intended to promote the implementation of new methods and improvement of existing ones, and to support advanced students, researchers and professionals in their daily efforts to identify, understand and solve a number of open questions in these fields. 
      



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