 Bespoke Learning in Static Systems: Application to Learning Sub-surface Material Density FunctionDalia Chakrabarty
Chapter Chapter 4 in Learning in the Absence of Training Data, 2023, pp 153-188 from Springer Abstract: Abstract In this chapter we discuss bespoke learning of a system property in a static system, i.e. discuss bespoke learning of the relevant system property parameter, at design values of an input. Such learning will then capacitate the supervised learning of the system property as a function of the input variable. This bespoke learning typically requires anticipating trends in values of the likelihood, as it changes with the difference between observations and (a model of) the system. Modelling the likelihood in terms of such a difference—or distance—is possible, subsequent to mapping from the space of system parameters onto the space of the observable. Here, we discuss an application of this scheme, to the non-destructive learning of the material density, as a function of a sub-surface location—using existing work that provides the bespoke learnt values of this density at designed sub-surface locations. The training data that is thus generated, bears strong inhomogeneities in correlation, rendering supervised learning of the material density function, difficult. This is undertaken, to enable prediction of the density at test locations. Date: 2023 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-31011-9_4 Ordering information: This item can be ordered from DOI: 10.1007/978-3-031-31011-9_4 Access Statistics for this chapter More chapters in Springer Books from Springer |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.