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- W2033728239 abstract "Artificial neural networks have, in recent years, been very successfully applied in a wide range of areas. A major reason for this success has been the existence of a training algorithm called backpropagation. This algorithm relies upon the neural units in a network having input/output characteristics that are continuously differentiable. Such units are significantly less easy to implement in silicon than are neural units with Heaviside (step-function) characteristics. In this paper, we show how a training algorithm similar to backpropagation can be developed for 2-layer networks of Heaviside units by treating the network weights (i.e., interconnection strengths) as random variables. This is then used as a basis for the development of a training algorithm for networks with any number of layers by drawing upon the idea of internal representations. Some examples are given to illustrate the performance of these learning algorithms." @default.
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- W2033728239 date "1995-11-01" @default.
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- W2033728239 title "The use of random weights for the training of multilayer networks of neurons with Heaviside characteristics" @default.
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- W2033728239 doi "https://doi.org/10.1016/0895-7177(95)00180-a" @default.
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