Abstract
A definition of maximal neighbourhoods in the form of hyperspheres around points in metric spaces is presented. An approximate algorithm to compute the radii of these hyperspheres is described. An exact algorithm to solve the same problem is described next. This exact algorithm is shown to be capable of handling two simple generalisations of the problem scenario. Usefulness of hyperspherical neighbourhoods around points representing patterns in a neural network model is demonstrated by means of numerical simulations.
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