Award details

Kernal methods for growth domain modelling and experimental design

Reference	D17534
Principal Investigator / Supervisor	Dr Gavin Cawley
Co-Investigators / Co-Supervisors	Dr Robert Foxall, Professor Michael William Peck
Institution	University of East Anglia
Department	Computing Sciences
Funding type	Research
Value (£)	71,172
Status	Completed
Type	Research Grant
Start date	15/04/2002
End date	26/12/2003
Duration	21 months

Abstract

Experimental research into the growth of microbial pathogens forms a vital component of efforts to ensure food safety, however statistical analysis of the results remains somewhat crude. We aim to develop a methodology for improved statistical analysis and modelling of experimental data. The work will benefit from recent advances in kernel methods, currently the one of the most active areas in the field of machine learning. The adoption of a Bayesian framework will support active learning, whereby a model of the available data is used to direct the design of further experimental work to obtain the greatest decrease in the uncertainty in predictions made by the subsequent generation of growth domain models.

Summary

unavailable

Committee	Closed Committee - Agri-food (AF)
Research Topics	X – not assigned to a current Research Topic
Research Priority	X – Research Priority information not available
Research Initiative	X - not in an Initiative
Funding Scheme	X – not Funded via a specific Funding Scheme

I accept the terms and conditions of use (opens in new window)

export PDF file

back to list new search