Transcription of Neural Networks and Statistical Models
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Neural Networks and Statistical Models
Neural Networks and Statistical Models Proceedings of the Nineteenth Annual SAS Users Group International Conference, April, 1994. Warren S. Sarle, SAS Institute Inc., Cary, NC, USA. Abstract intelligent in the usual sense of the word. Artificial Neural Networks learn in much the same way that many Statistical There has been much publicity about the ability of artificial Neural algorithms do estimation, but usually much more slowly than Networks to learn and generalize. In fact, the most commonly Statistical algorithms. If artificial Neural Networks are intelligent, used artificial Neural Networks , called multilayer perceptrons, are then many Statistical methods must also be considered intelligent. nothing more than nonlinear regression and discriminant Models Few published works provide much insight into the relationship that can be implemented with standard Statistical software.
ologists, psychologists, or computer scientists who know little about statistics and nonlinear optimization. NN researchers rou-tinely reinvent methods that have been known in the statistical or mathematical literature for decades or centuries, but they often fail to understand how these methods work (e.g., Specht 1991).
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