Using Bayesian neural networks to classify segmented images

F. Vivarelli, C.K.I. Williams

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We present results that compare the performance of neural networks trained with two Bayesian methods, (i) the evidence framework of D.J.C. MacKay (1992) and (ii) a Markov chain Monte Carlo method due to R.M. Neal (1996) on a task of classifying segmented outdoor images. We also investigate the use of the automatic relevance determination method for input feature selection
Original languageEnglish
Title of host publicationArtificial Neural Networks, Fifth International Conference on (Conf. Publ. No. 440)
Number of pages6
ISBN (Print)0-85296-690-3
Publication statusPublished - 1 Jul 1997


  • neural nets
  • Bayesian neural networks
  • Markov chain Monte Carlo method
  • automatic relevance determination method
  • evidence framework
  • input feature selection
  • performance
  • segmented images classification


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