Using Bayesian neural networks to classify segmented images

Francesco Vivarelli; Christopher K. I. Williams

doi:10.1049/cp:19970738

Using Bayesian neural networks to classify segmented images

Francesco Vivarelli, Christopher K. I. Williams

Aston University

Research output: Chapter in Book/Published conference output › Chapter

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 language	English
Title of host publication	Fifth International Conference on Artificial Neural Networks
Place of Publication	Cambridge, UK
Publisher	Aston University
Pages	268-273
Number of pages	6
Volume	1997
ISBN (Print)	0852966903
DOIs	https://doi.org/10.1049/cp:19970738
Publication status	Published - 9 Jul 1997

Keywords

neural networks
Automatic Relevance Determination
feature selection

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10.1049/cp:19970738

Using Bayesian neural networks to classify segmented imagesFinal published version, 594 KB

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AB - 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.

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VL - 1997

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BT - Fifth International Conference on Artificial Neural Networks

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ER -

Using Bayesian neural networks to classify segmented images

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Keywords

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