On the Use of Evidence in Neural Networks

@inproceedings{Wolpert1992OnTU,
  title={On the Use of Evidence in Neural Networks},
  author={David H. Wolpert},
  booktitle={NIPS},
  year={1992}
}
The Bayesian "evidence" approximation, which is closely related to generalized maximum likelihood, has recently been employed to determine the noise and weight-penalty terms for training neural nets. This paper shows that it is far simpler to perform the exact calculation than it is to set up the evidence approximation. Moreover, unlike that approximation, the exact result does not have to bere-calculated for every new data set. Nor does it require the running of complex numerical computer code… CONTINUE READING

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