Using Bayesian neural networks to classify segmented images

Francesco Vivarelli, Christopher K. I. Williams

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Abstract

    We present results that compare the performance of neural networks trained with two Bayesian methods, (i) the Evidence Framework of MacKay (1992) and (ii) a Markov Chain Monte Carlo method due to 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 publicationFifth International Conference on Artificial Neural Networks
    Place of PublicationCambridge, UK
    PublisherAston University
    Pages268-273
    Number of pages6
    Volume1997
    ISBN (Print)0852966903
    DOIs
    Publication statusPublished - 9 Jul 1997

    Keywords

    • neural networks
    • Automatic Relevance Determination
    • feature selection

    Fingerprint Dive into the research topics of 'Using Bayesian neural networks to classify segmented images'. Together they form a unique fingerprint.

    Cite this