Uncertainty quantification in classification problems:A Bayesian approach for predicting the effects of further test sampling

Phillipson, Jordan and Blair, Gordon and Henrys, Peter (2019) Uncertainty quantification in classification problems:A Bayesian approach for predicting the effects of further test sampling. In: Proceedings of MODSIM2019, 23rd International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Canberra, pp. 193-199. ISBN 9780975840092

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Abstract

The use of machine learning techniques in classification problems has been shown to be useful in many applications. In particular, they have become increasingly popular in land cover mapping applications in the last decade. These maps often play an important role in environmental science applications as they can act as inputs within wider modelling chains and in estimating how the overall prevalence of particular land cover types may be changing.

Item Type:
Contribution in Book/Report/Proceedings
Subjects:
ID Code:
140379
Deposited By:
Deposited On:
23 Jan 2020 10:30
Refereed?:
Yes
Published?:
Published
Last Modified:
03 Aug 2020 00:36