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Using Under-Trained Deep Ensembles to Learn Under Extreme Label Noise: A Case Study for Sleep Apnea Detection.

Konstantinos NikolaidisThomas PlagemannStein KristiansenVera GoebelMohan S. Kankanhalli
Published in: IEEE Access (2021)
Keyphrases
  • sleep apnea
  • label noise
  • training set
  • automatic analysis
  • active learning
  • multi class
  • machine learning
  • decision trees
  • object detection
  • noise model
  • noise tolerant
  • sleep stage
  • obstructive sleep apnea