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| | comp.ai.neural-nets FAQ, Part 1 of 7: Introduction |
 | | To be somewhat more precise, feedforward networks with a single hidden layer and trained by least-squares are statistically consistent estimators of arbitrary square-integrable regression functions under certain practically-satisfiable assumptions regarding sampling, target noise, number of hidden units, size of weights, and form of hidden-unit activation function (White, 1990). |
 | | Feedforward networks with a single hidden layer using threshold or sigmoid activation functions are universally consistent estimators of binary classifications (Faragó and Lugosi, 1993; Lugosi and Zeger 1995; Devroye, Györfi, and Lugosi, 1996) under similar assumptions. |
 | | Feedforward nets are a subset of the class of nonlinear regression and discrimination models. |
| www.uni-giessen.de /faq/archiv/ai-faq.neural-nets.part1-7/msg00000.html (12876 words) |
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