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- W73642277 abstract "SummaryA class of artificial neural networks (ANN) are interpreted as complex multivariate statistical models for the approximation of an unknown expectation function of a random variable y given an explanatory variable x. Thus, the weights of these ANN can be viewed as parameters, which can be estimated by statistical methods. Important network models are compared with equivalent statistical models. The closeness of approximation is evaluated by distance functions. Quasi Maximum Likelihood and nonlinear least squares methods can be used as estimation methods, depending on the chosen distance function. Estimation algorithms (Newton-Raphson, weight decay, adaptive stepsize, generalized delta rule, etc.) are discussed and some significance tests are described." @default.
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- W73642277 date "1996-01-01" @default.
- W73642277 modified "2023-09-23" @default.
- W73642277 title "Statistical Models and Artificial Neural Networks" @default.
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- W73642277 doi "https://doi.org/10.1007/978-3-642-80098-6_21" @default.
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