18 Inductive Conformal Prediction : Theory and Application to Neural Networks

@inproceedings{Papadopoulos200818IC,
  title={18 Inductive Conformal Prediction : Theory and Application to Neural Networks},
  author={Harris Papadopoulos},
  year={2008}
}
Traditional machine learning algorithms for pattern recognition just output simple predictions, without any associated confidence values. Confidence values are an indication of how likely each prediction is of being correct. In the ideal case, a confidence of 99% or higher for all examples in a set, means that the percentage of erroneous predictions in that set will not exceed 1%. Knowing the likelihood of each prediction enables us to assess the extent to which we can rely on it. For this… CONTINUE READING
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Normalized nonconformity measures for regression conformal prediction

  • H. Papadopoulos, A. Gammerman, V. Vovk
  • Proceedings of the IASTED International…
  • 2008
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