• DocumentCode
    2292650
  • Title

    Using Bayesian neural networks to classify segmented images

  • Author

    Vivarelli, Francesco ; Williams, Christopher K I

  • Author_Institution
    Neural Comput. Res. Group, Aston Univ., Birmingham, UK
  • fYear
    1997
  • fDate
    7-9 Jul 1997
  • Firstpage
    268
  • Lastpage
    273
  • Abstract
    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
  • Keywords
    neural nets; Bayesian neural networks; Markov chain Monte Carlo method; automatic relevance determination method; evidence framework; input feature selection; performance; segmented images classification;
  • fLanguage
    English
  • Publisher
    iet
  • Conference_Titel
    Artificial Neural Networks, Fifth International Conference on (Conf. Publ. No. 440)
  • Conference_Location
    Cambridge
  • ISSN
    0537-9989
  • Print_ISBN
    0-85296-690-3
  • Type

    conf

  • DOI
    10.1049/cp:19970738
  • Filename
    607529
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2292650