title: Approximation techniques for neuromimetic calculus.
abstract: Approximation Theory plays a central part in modern statistical methods, in particular in Neural Network modeling. These models are able to approximate a large amount of metric data structures in their entire range of definition or at least piecewise. We survey most of the known results for networks of neurone-like units. The connections to classical statistical ideas such as ordinary least squares (LS) are emphasized.
authors.affiliation: CEMIF, Université d'Evry, France.
authors.forename: V
authors.initials: V
authors.lastname: Vigneron
authors.forename: C
authors.initials: C
authors.lastname: Barret
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008433:Mathematics; D016571:Neural Networks, Computer; D013223:Statistics as Topic / Q000379:methods