This makes backpropagation-orsimply backprop-theconnectionists' master algorithm.
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Combining connectionism and evolutionism was fairly easy: just evolve the network structure and learn the parameters by backpropagation.
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To train SNNs with supervision, we propose an efficient on-chip training scheme approximating backpropagation algorithm suitable for hardware implementation.
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The connectionists' master algorithm is backpropagation, which they use to figure out which neurons are responsible for which errors and adjust their weights accordingly.
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The aim of this study is to develop backpropagation neural networks (BPNN) for better prediction of ventilatory function in children and adolescents.
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A three layer backpropagation neural network containing 400 input nodes, 20 intermediate nodes, and one output node was able to forms.
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Backpropagation, as this algorithm is known, is phenomenally more powerful than the perceptron algorithm.
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Backpropagation is well suited to this problem because of its ability to efficiently learn nonlinear functions.
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Backpropagation is a form of gradient descent, which Alchemy uses to optimize the weights of a Markov logic network.