• Active Learning for Data Streams 

      Cacciarelli, Davide (Doctoral theses at NTNU;2024:186, Doctoral thesis, 2024)
      As businesses increasingly rely on machine learning models to make informed decisions, the ability to develop accurate and reliable models is critical. However, in many industrial contexts, data annotation represents a ...
    • A novel fault detection and diagnosis approach based on orthogonal autoencoders 

      Cacciarelli, Davide; Kulahci, Murat (Peer reviewed; Journal article, 2022)
      In recent years, there have been studies focusing on the use of different types of autoencoders (AEs) for monitoring complex nonlinear data coming from industrial and chemical processes. However, in many cases the focus ...
    • Robust online active learning 

      Cacciarelli, Davide; Kulahci, Murat; Tyssedal, John Sølve (Journal article; Peer reviewed, 2023)
      In many industrial applications, obtaining labeled observations is not straightforward as it often requires the intervention of human experts or the use of expensive testing equipment. In these circumstances, active learning ...
    • Stream-based active learning with linear models 

      Cacciarelli, Davide; Kulahci, Murat; Tyssedal, John Sølve (Journal article; Peer reviewed, 2022)