Approximation Techniques for Neuromimetic Calculus
Vincent Vigneron, C. Barret · International Journal of Neural Systems · 1999
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.