Stability of neural networks for slightly perturbed training data sets

Indrias Berhane, C. Srinivasan

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


In learning models of artificial neural networks, that randomness comes from the distribution of the training data. We show individual observations do not affect excessively for a neutral network modeling, provided that it has adequate nodes on the hidden layer and proves that the empirical error of a neural network with p number of weights converges to the expected error when p/ m → 0 where m is the size of the perturbed training data.

Original languageEnglish
Pages (from-to)2259-2270
Number of pages12
JournalCommunications in Statistics - Theory and Methods
Issue number9 SPEC.ISS.
StatePublished - Sep 2004


  • Artificial neural networks
  • Stability

ASJC Scopus subject areas

  • Statistics and Probability


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