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Autor(en): 
  • Chen Zhang
  • Di Jiang
  • Yuanfeng Song
  • Probabilistic Topic Models: Foundation and Application 
     

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


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 7-14 Tagen versandfertig
    Veröffentlichung:  Juni 2023  
    Genre:  EDV / Informatik 
     
    Bayesiannetwork / Computerlinguistik und Korpuslinguistik / Datenbanken / Datenmanagement / Graphicalmodel / machinelearning / Maschinelles Lernen / NaturalLanguageProcessing / Theoretische Informatik
    ISBN:  9789819924301 
    EAN-Code: 
    9789819924301 
    Verlag:  Springer 
    Einband:  Gebunden  
    Sprache:  English  
    Dimensionen:  H 241 mm / B 160 mm / D 15 mm 
    Gewicht:  412 gr 
    Seiten:  160 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    This book introduces readers to the theoretical foundation and application of topic models. It provides readers with efficient means to learn about the technical principles underlying topic models. More concretely, it covers topics such as fundamental concepts, topic model structures, approximate inference algorithms, and a range of methods used to create high-quality topic models. In addition, this book illustrates the applications of topic models applied in real-world scenarios. Readers will be instructed on the means to select and apply suitable models for specific real-world tasks, providing this book with greater use for the industry. Finally, the book presents a catalog of the most important topic models from the literature over the past decades, which can be referenced and indexed by researchers and engineers in related fields. We hope this book can bridge the gap between academic research and industrial application and help topic models play an increasingly effective role inboth academia and industry. This book offers a valuable reference guide for senior undergraduate students, graduate students, and researchers, covering the latest advances in topic models, and for industrial practitioners, sharing state-of-the-art solutions for topic-related applications. The book can also serve as a reference for job seekers preparing for interviews.

      



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