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Latin hypercube sampling is used to generate initial weights and biases of the network.
Adjusted weights and biases of the network are determined from the training set and the test set is used for calibration to prevent overtraining networks.
Optimization of network performance can be ensured during the training process which involves fine tuning the values of weights and biases of the network.
During the training of the network, the weights and biases of the network were iteratively adjusted to minimize the network performance with a back-propagation algorithm.
During the training of the neural network, the weights and biases of the network were updated until its mean squared error was less than a target mean squared error.
where y k ( p ) and t k ( p ) denote the network outputs and target responses, respectively, of each output node k given a pattern p, and w is a vector containing all the weights and biases of the network.
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Some contemporary analysts lamented the export bias of the network configuration, while opposing the "monopoly" of private British companies on nationalist grounds.
While the first approach fairly tests how well a network comparison method distinguishes between different graph families without the bias of the network sizes and densities, the second approach should be taken with more caution, as any observed difference between networks could be due not to the actual differences in network topologies but differences in network sizes and densities.
The purpose of combining these algorithms with the neural network is to optimize weights and biases of the neural network.
He continued, "The conservative part of this country, while talking about the liberal bias of the networks and the New York media, tends to speak more of Dan Rather representing that".
Finally, the MRC offers no baseline from which to judge the relative purported "liberal bias" of the networks.
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