Comparing Bayesian neural network algorithms for classifying segmented outdoor images.

F Vivarelli; C K Williams

doi:10.1016/s0893-6080(01)00024-7

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Comparing Bayesian neural network algorithms for classifying segmented outdoor images.

F Vivarelli·C K Williams

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Abstract

In this paper we investigate the Bayesian training of neural networks for region labelling of segmented outdoor scenes; the data are drawn from the Sowerby Image Database of British Aerospace. Neural networks are trained with two Bayesian methods, (i) the evidence framework of MacKay (1992a,b) and (ii) a Markov Chain Monte Carlo method due to Neal (1996). The performance of the two methods is compared to evaluating the empirical learning curves of neural networks trained with the two methods. We also investigate the use of the Automatic Relevance Determination method for input feature selection.

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Algorithms Artificial Intelligence Bayes Theorem Markov Chains Monte Carlo Method Neural Networks, Computer Pattern Recognition, Automated Comparative Study Journal Article Research Support, Non-U.S. Gov't

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F Vivarelli, C K Williams. 2001. Comparing Bayesian neural network algorithms for classifying segmented outdoor images.. https://doi.org/10.1016/s0893-6080(01)00024-7

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