IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-662-66278-6_5.html
   My bibliography  Save this book chapter

Strukturierte und unstrukturierte Daten

In: Künstliche Intelligenz und Data Science in Theorie und Praxis

Author

Listed:
  • Wolfgang Reuter

    (Alexander Thamm GmbH)

  • Anna Korotkova

    (Alexander Thamm GmbH)

  • Linh Nguyen

    (Alexander Thamm GmbH)

Abstract

Zusammenfassung Statistische Methoden und maschinelle Lernverfahren kann man sich vorstellen als Algorithmen, die einen Datensatz als „Eingabe“ erhalten und ein Modell, eine Parameterschätzung oder eine Prädiktion als „Ausgabe“ liefern. An die Eingabe werden dabei in der Regel spezielle Erwartungen gerichtet, vor allem im Hinblick auf die Struktur und das Format der Daten. Die meisten Methoden operieren auf „tabellarischen“ Daten, also Daten in Form einer Tabelle, in der jede Zeile zu einer Beobachtung korrespondiert und jede Spalte einer Variablen entspricht (synonym werden hier auch Begriffe wie Attribut oder Feature verwendet).

Suggested Citation

  • Wolfgang Reuter & Anna Korotkova & Linh Nguyen, 2023. "Strukturierte und unstrukturierte Daten," Springer Books, in: Andreas Gillhuber & Göran Kauermann & Wolfgang Hauner (ed.), Künstliche Intelligenz und Data Science in Theorie und Praxis, chapter 0, pages 51-68, Springer.
    • Handle: RePEc:spr:sprchp:978-3-662-66278-6_5
    DOI: 10.1007/978-3-662-66278-6_5
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-662-66278-6_5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.