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Using Bayesian neural networks to classify segmented images

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

Original languageEnglish
Title of host publicationArtificial Neural Networks, Fifth International Conference on (Conf. Publ. No. 440)
PublisherIET
Pages268-273
Number of pages6
ISBN (Print)0-85296-690-3
DOIs
Publication statusPublished - 1 Jul 1997

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

    Research areas

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

ID: 21958998