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It can be used, therefore, to evaluate performance differences between the training process carried out on a computer and its hardware implementation.
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The M-PLN model attempts to strengthen the discrepancies between distinct patterns used during the training process and those that have not yet been processed.
The generalization performance of a SVM depends on parameters regulating the trade-off between accuracy and complexity in the training process [32] and the kernel-specific parameters.
Using a multimodal approach, we identified differences in the training process between the roles of leader and follower.
Participants were active in the training process between 1.5 and 3.5 hours daily, 6 days a week.
Each tree can potentially provide a different classification since the training process is randomized between trees.
The network parameters are initialized by small random numbers between 0.0 and 1.0, and are adapted during the training process.
The training process continued until the difference between the ANN classification and clinical diagnosis became diminished.
A modified Levenberg-Marquardt algorithm is developed for the training of BP neural network, which can back propagate the errors between entire actual system and the constructed dynamic model into the training process of the neural network.
ANNs have the advantage over the fuzzy inference systems that knowledge is automatically gained during the training process by updating the connection weights between neurons [21].
The training process includes simple computations of Euclidian distances between the training vectors and is easily performed even in cases of very small training sets.
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