Convolutional Neural Networks

Research output: Contributions to collected editions/worksChapter

Authors

Dieses Kapitel führt in Convolutional Neural Networks (CNNs) ein und beschreibt, wie diese im Kontext der Sportanalyse verwendet werden können. Insbesondere eignen sich CNNs für das End-to-End-Lernen auf Bildern oder ähnlich strukturierten Daten. Dabei können CNNs Merkmale von Bildern anhand der Pixelwerte effizient lernen und beispielsweise sehr gute Merkmale für eine Klassifikationsaufgabe extrahieren. Die Modelle profitieren dabei von Parameter Sharing in den Convolutional Layern und zeichnen sich durch (bedingte) Translationsäquivarianz und -invarianz aus. CNNs eignen sich auch dafür, Merkmale aus Positionsdaten von Teamsportarten zu lernen, sofern die Daten in eine entsprechende Struktur gebracht werden.
Original languageEnglish
Title of host publicationSportinformatik : Modellbildung, Simulation, Datenanalyse und Visualisierung von sportbezogenen Daten
EditorsDaniel Memmert
Number of pages9
Place of PublicationBerlin
PublisherSpringer Spektrum
Publication date01.01.2023
Pages207-215
ISBN (print)978-3-662-67025-5
ISBN (electronic)978-3-662-67026-2
DOIs
Publication statusPublished - 01.01.2023

Bibliographical note

© Der/die Autor(en), exklusiv lizenziert an Springer-Verlag GmbH, DE, ein Teil von Springer Nature 2023

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