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Drifterkennung und –behandlung

In: Online Machine Learning

Author

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  • Thomas Bartz-Beielstein

    (TH Köln, Institut Data Science, Engineering, and Analytics)

Abstract

Zusammenfassung Drift wurde in Abschnitt 1.2.2 eingeführt. Dieses Kapitel behandelt Methoden zur Drifterkennung und -behandlung, insbesondere für Konzeptdrift. Für die in Kapitel 2 dargestellten Algorithmen wird geklärt, inwiefern auf Konzeptdrift reagiert wird. Inwiefern im Gegenzug das katastrophale Vergessen ein Thema ist, wird in Abschnitt 4.3 beschrieben. Abschnitt 3.1 beschreibt drei Architekturen für die Implementierung von Drifterkennungsalgorithmen. Abschnitt 3.2 beschreibt grundlegende Eigenschaften fensterbasierter Ansätze. Abschnitt 3.3 stellt häufig verwendete Verfahren zur Drifterkennung vor. Abschnitt 3.4 beschreibt, wie die in Abschnitt 3.3 eingeführten Verfahren zur Drifterkennung in OML-Algorithmen zum Einsatz kommen und stellt die im Paket River implementierten baumbasierten OML-Verfahren zusammenfassend dar.

Suggested Citation

  • Thomas Bartz-Beielstein, 2024. "Drifterkennung und –behandlung," Springer Books, in: Thomas Bartz-Beielstein & Eva Bartz (ed.), Online Machine Learning, edition 2, chapter 0, pages 25-39, Springer.
    • Handle: RePEc:spr:sprchp:978-3-658-46162-1_3
    DOI: 10.1007/978-3-658-46162-1_3
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