title: An approach to guaranteeing generalisation in neural networks.
abstract: A novel approach to generalisation is presented that is able, under certain circumstances, to guarantee the generalisation to binary-output data for which no targets have been given. The basis of the guarantee is the recognition of a persistent global minimum error solution. An empirical test for whether the guarantee holds is provided which uses a technique called target reversal. The technique employs two neural networks whose convergence using opposing targets signals validity of the guarantee.
authors.affiliation: Land Use Change Programme, Macaulay Land Use Research Institute, Aberdeen, Scotland, UK. g.polhill@mluri.sari.ac.uk
authors.forename: J G
authors.initials: JG
authors.lastname: Polhill
authors.forename: M K
authors.initials: MK
authors.lastname: Weir
mesh_terms: D001499:Bayes Theorem; D016571:Neural Networks, Computer; D015203:Reproducibility of Results