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Bayesian neural network learning for prediction in the Australian dairy industry

Macrossan, Paula E., Abbass, Hussein A., Mengersen, Kerrie L., Towsey, Michael W., & Finn, Gerard (1999) Bayesian neural network learning for prediction in the Australian dairy industry. In Hand, D.J., Kok, J.N., & Berthold, M.R. (Eds.) Third International Symposium on Intelligent Data Analysis (IDA'99), August, 1999, Netherlands.

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Abstract

One of the most common problems encountered in agriculture is that of predicting a response variable from covariates of interest. The aim of this paper is to use a Bayesian neural network approach to predict dairy daughter milk production from dairy dam, sire, herd and environmental factors. The results of the Bayesian neural network are compared with results obtained when the regression relationship is described using the traditional neural network approach. In addition, the "baseline" results of a multiple linear regression employing both frequentist and Baysian methods are presented. The potential advantages of the Bayesian neural network appraoch over the traditional neural network approach are discussed.

Impact and interest:

2 citations in Web of Science®
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ID Code: 7607
Item Type: Conference Paper
Additional Information: For more information, please refer to the publisher's website (see hypertext link) or contact the author.
Keywords: Bayesian neural network, milk production
DOI: 10.1007/3-540-48412-4_33
ISBN: 9783540663324
ISSN: 0302-9743
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Neural Evolutionary and Fuzzy Computation (080108)
Australian and New Zealand Standard Research Classification > AGRICULTURAL AND VETERINARY SCIENCES (070000) > ANIMAL PRODUCTION (070200) > Animal Breeding (070201)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 1999 Springer
Deposited On: 15 May 2007
Last Modified: 15 Jan 2009 17:32

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