Approximation techniques for neuromimetic calculus.
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.
Vigneron
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CEMIF, Université d'Evry, France.
Barret
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C
D000465:Algorithms; D003198:Computer Simulation; D008433:Mathematics; D016571:Neural Networks, Computer; D013223:Statistics as Topic / Q000379:methods