title: Function approximation on non-Euclidean spaces.
abstract: This paper presents a family of layered feed-forward networks that is able to uniformly approximate functions on any metric space, and also on a wide variety of non-metric spaces. Non-Euclidean input spaces are frequently encountered in practice, while usual approximation schemes are guaranteed to work only on Euclidean metric spaces. Theoretical foundations are provided, as well as practical algorithms and illustrative examples. This tool potentially constitutes a significant extension of the common notion of 'universal approximation capability'.
authors.affiliation: Laboratoire de Psychologie Cognitive, CNRS-UMR 6146, Université de Provence, 29 avenue Robert Schuman, 13621 Aix-en-Provence cedex 1, France. courrieu@up.univ-mrs.fr
authors.forename: Pierre
authors.initials: P
authors.lastname: Courrieu
mesh_terms: D000465:Algorithms; D016018:Least-Squares Analysis; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D013028:Space Perception / Q000502:physiology*; D013569:Synapses / Q000502:physiology