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Yates J dissented, on the grounds that the focus on the author obscured the impact this decision would have on "the rest of mankind", which he felt would be to create a virtual monopoly, something that would have a detrimental impact on the public and should certainly not be considered "an encouragement of the propagation of learning".
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The outputs (predicted concentrations) are compared with targets (actual concentrations) and the difference between them is called the error which is back propagated (and so called feed-forward ANN with the back propagation of errors learning algorithm) to network once more to be minimized through further adjustment of weights.
The method is based on the use of Belief Propagation for learning through Entropy Maximization on both the Stochastic Block Model (SBM) and the degree-corrected Stochastic Block Model (dcSBM).
The type of ANN used in this paper is feed-forward model which was trained with the back propagation of errors learning algorithm.
A back-propagation (BP) algorithm is a common method of learning ANNs.
We analyzed various back-propagation learning algorithms, which have an influence on system performance as well as the speed of learning.
The propagation of machine learning based property prediction methods (e.g. QSAR, QSPR,.…) has lead to the question of the reliability of the prediction.
This is called backward propagation after learning (Holroyd & Coles, 2002).
The simplest implementation of back-propagation learning updates the network weights and biases in the direction of performance function decreasing.
The ANN predictive models of surface roughness was developed using a multilayer feed forward neural network and trained with the help of an error back propagation learning algorithm based on the generalized delta rule.
The parameters of the ANN model are adjusted by the back propagation learning algorithm using wide ranges of experimental datasets.
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