Browsing by Subject "machine learning"

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  • Paul, Andreas; Rottensteiner, Franz; Heipke, Christian (Göttingen : Copernicus GmbH, 2016)
    Domain adaptation techniques in transfer learning try to reduce the amount of training data required for classification by adapting a classifier trained on samples from a source domain to a new data set (target domain) ...
  • Hu, Tingli; Kühn, Johannes; Matouq, Jumana; Haddadin, Sami (Hannover : Institutionelles Repositorium der Leibniz Universität Hannover, 2018-05-16)
    As a pilot study, we present 1)a database that includes 30 daily-life table-top tasks, which were selected within the SoftPro project, and 2)a novel autoencoder-based muscle synergy identification method, whose results ...
  • Wedel, Frederik; Käding, Max; Marx, Steffen (Hannover : Institutionelles Repositorium der Leibniz Universität Hannover, 2019)
    Die Schallemissionsanalyse zur Spanndrahtbrucherkennung etabliert sich in Deutschland als Verfahren zur Überwachung vorgespannter Konstruktionen. Das Interesse liegt dabei auf der zuverlässigen Erkennung von Spanndrahtbrüchen, ...
  • Zhuang, Xiaoying; Zhou, Shuai (Henderson : Tech Science Press, 2019)
    Advances in machine learning (ML) methods are important in industrial engineering and attract great attention in recent years. However, a comprehensive comparative study of the most advanced ML algorithms is lacking. Six ...