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Statistical transfer learning: A review and some extensions to statistical process control

Summary: The rapid development of information technology, together with advances in sensory and data acquisition techniques, has led to the increasing necessity of handling datasets from multiple domains. In recent years, transfer learning has emerged as an effective framework for tackling related tasks in target domains by transferring previously-acquired knowledge from source domains. Statistical models and methodologies are widely involved in transfer learning and play a critical role, which, however, has not been emphasized in most surveys of transfer learning. In this article, we conduct a comprehensive literature review on statistical transfer learning, i.e., transfer learning techniques with a focus on statistical models and statistical methodologies, demonstrating how statistics can be used in transfer learning. In addition, we highlight opportunities for the use of statistical transfer learning to improve statistical process control and quality control. Several potential future issues in statistical transfer learning are discussed.

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  • Topics: Statistics
  • Keywords: 3D printing, Bayesian modeling, Landslides, Quality control, Regularization, Statistical process control, Statistical transfer learning, Transfer learning, Urban rail transit
  • Author: Tsung, Fugee; Zhang, Ke; Cheng, Longwei; Song, Zhenli
  • Journal: Quality Engineering