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Filling In The Gap: A General Method Using Neural Networks.pdf

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🔗 Source: cinc.org
📊 Size: 88 KB
📄 Pages: 4 pages
⬇️ Downloads: 85

Summary

A general method using neural networks to fill in gaps in medical signals is presented. The method involves training a multilayer perceptron (MLP) to reconstruct a missing signal from other available signals. The MLP is trained using a procedure adapted from Geoffrey Hinton's ideas, which involves creating an autoencoder for the input signals and another for the target signal. The autoencoders are initialized with the weights of a stack of Restricted Boltzmann Machines (RBMs), which are trained using Contrastive Divergence (CD) learning and Mean Field learning. The method was applied to the PhysioNet/Computing in Cardiology Challenge 2010 dataset and achieved the best scores among participants.

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Description

A general method using neural networks to fill in gaps in medical signals is presented.

Technical Information

  • File Format: PDF
  • File Size: 88 KB
  • Pages: 4
  • Language: EN
  • Total Downloads: 85
  • Last Updated: 1 month ago

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