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A genetic algorithms based multi-objective optimization technique was utilized in the training process of a feed forward neural network, using noisy data from an industrial iron blast furnace.
To test the best structures of the trained RBF and MLP networks, the identified structure from each approach is separately deployed to the same unknown test data, which were not utilized in the training process.
These methods utilize the unlabeled target instances during the training process and generally either infer their labels, align the source data (or source prediction function) to that of the target domain, incorporate them into the mapping process, or utilize them otherwise to represent the target domain.
That streamlines the training process.
The training process has taken two years.
The training process can be pretty humbling.
Amref had already begun the training process.
After the training process, the model was validated.
The training process is stopped.
The training process uses supervised learning.
Fig. 1 Diagram of the training process.
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