Identification from Noisy Examples

  • Philip D. Laird
Part of the The Kluwer International Series in Engineering and Computer Sciences book series (SECS, volume 47)


When some of the training examples may be incorrect, none of the foregoing identification strategies are effective:
  • With algorithms based on identification by enumeration (including refinement algorithms), an incorrect example may cause a correct hypothesis to be discarded. Even if correct hypotheses occur infinitely often in the enumeration, errors can cause them to be discarded infinitely often, and thereby frustrate convergence.

  • With pac-identification, the fundamental strategy of choosing a hypothesis consistent with the examples may fail, because there may be no such hypothesis.


Good Rule Noise Rate Content Error Correct Rule Correct Hypothesis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Kluwer Academic Publishers 1988

Authors and Affiliations

  • Philip D. Laird
    • 1
  1. 1.NASA Ames Research CenterUSA

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