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It is well established that including monotonic constraints in neural network (NN) development, when such a priori information is available, increases the confidence in predictions and prevents over fitting. An elegant procedure to guarantee mathematically monotonic NNs with respect to some of their inputs is to force the signs of some of the network's weights in such a way that the network remains...
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