Towards Better Performance in the Face of Input Uncertainty while Maintaining Interpretability in AI
Uncertainty is a pervasive element of many real-world applications and very often existing sources of uncertainty (e.g. atmospheric conditions, economic parameters or precision of measurement devices) have a detrimental impact on the input and ultimately results of decision-support systems. Thus, th...
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
| Published: |
2021
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| Online Access: | https://eprints.nottingham.ac.uk/66082/ |