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Autor(en): 
  • Geoff Dougherty
  • Pattern Recognition and Classification: An Introduction 
     

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


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Oktober 2012  
    Genre:  EDV / Informatik 
     
    Algorithms / Applications of Nonlinear Dynamics and Chaos Theory / Automated Pattern Recognition / B / bioinformatics / Biology, life sciences / Computational and Systems Biology / Computational biology / Computer Appl. in Life Sciences / computer science / Digital and Analog Signal Processing / Dynamics & statics / Image processing / Imaging systems & technology / Information technology# general issues / Nonlinear Optics / Nonlinear science / Numerical analysis / pattern recognition / Signal Processing / Signal, Image and Speech Processing / Speech processing systems / Statistical physics
    ISBN:  9781461453222 
    EAN-Code: 
    9781461453222 
    Verlag:  Springer Nature EN 
    Einband:  Gebunden  
    Sprache:  English  
    Dimensionen:  H 235 mm / B 155 mm / D 20 mm 
    Gewicht:  483 gr 
    Seiten:  196 
    Illustration:  XI, 196 p. 158 illus., 104 illus. in color. With online files/update., schwarz-weiss Illustrationen, farbige Illustrationen 
    Zus. Info:  Book w. online files / update 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner.

    Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in image and signal processing and analysis, and in computer vision. Fundamental concepts of supervised and unsupervised classification are presented in an informal, rather than axiomatic, treatment so that the reader can quickly acquire the necessary background for applying the concepts to real problems. More advanced topics, such as semi-supervised classification, combining clustering algorithms and relevance feedback are addressed in the laterchapters.

    This book is suitable for undergraduates and graduates studying pattern recognition and machine learning.
      
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