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

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Phillipson, J, Blair, G & Henrys, P 2019, Uncertainty quantification in classification problems: A Bayesian approach for predicting the effects of further test sampling. in S Elsawah (ed.), Proceedings of MODSIM2019, 23rd International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Canberra, pp. 193-199. https://doi.org/10.36334/modsim.2019.B1.phillipson

APA

Phillipson, J., Blair, G., & Henrys, P. (2019). Uncertainty quantification in classification problems: A Bayesian approach for predicting the effects of further test sampling. In S. Elsawah (Ed.), Proceedings of MODSIM2019, 23rd International Congress on Modelling and Simulation (pp. 193-199). Modelling and Simulation Society of Australia and New Zealand (MSSANZ). https://doi.org/10.36334/modsim.2019.B1.phillipson

Vancouver

Phillipson J, Blair G, Henrys P. Uncertainty quantification in classification problems: A Bayesian approach for predicting the effects of further test sampling. In Elsawah S, editor, Proceedings of MODSIM2019, 23rd International Congress on Modelling and Simulation. Canberra: Modelling and Simulation Society of Australia and New Zealand (MSSANZ). 2019. p. 193-199 doi: 10.36334/modsim.2019.B1.phillipson

Author

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

Bibtex

@inproceedings{696b3076a129431591d40cae3c31f9f5,
title = "Uncertainty quantification in classification problems: A Bayesian approach for predicting the effects of further test sampling",
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. ",
keywords = "Uncertainty quantification, land cover mapping, Bayesian, sampling strategies",
author = "Jordan Phillipson and Gordon Blair and Peter Henrys",
year = "2019",
month = dec,
day = "6",
doi = "10.36334/modsim.2019.B1.phillipson",
language = "English",
isbn = "9780975840092",
pages = "193--199",
editor = "S. Elsawah",
booktitle = "Proceedings of MODSIM2019, 23rd International Congress on Modelling and Simulation",
publisher = "Modelling and Simulation Society of Australia and New Zealand (MSSANZ)",

}

RIS

TY - GEN

T1 - Uncertainty quantification in classification problems

T2 - A Bayesian approach for predicting the effects of further test sampling

AU - Phillipson, Jordan

AU - Blair, Gordon

AU - Henrys, Peter

PY - 2019/12/6

Y1 - 2019/12/6

N2 - 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.

AB - 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.

KW - Uncertainty quantification

KW - land cover mapping

KW - Bayesian

KW - sampling strategies

U2 - 10.36334/modsim.2019.B1.phillipson

DO - 10.36334/modsim.2019.B1.phillipson

M3 - Conference contribution/Paper

SN - 9780975840092

SP - 193

EP - 199

BT - Proceedings of MODSIM2019, 23rd International Congress on Modelling and Simulation

A2 - Elsawah, S.

PB - Modelling and Simulation Society of Australia and New Zealand (MSSANZ)

CY - Canberra

ER -